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1
Yadav, Kamini, and Hatim M. E. Geli. "Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period." Land 10, no. 12 (December 15, 2021): 1389. http://dx.doi.org/10.3390/land10121389.
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Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems.
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Mia, Md Suruj, Ryoya Tanabe, Luthfan Nur Habibi, Naoyuki Hashimoto, Koki Homma, Masayasu Maki, Tsutomu Matsui, and Takashi S. T. Tanaka. "Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data." Remote Sensing 15, no. 10 (May 10, 2023): 2511. http://dx.doi.org/10.3390/rs15102511.
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Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from model to model, although the prediction accuracy was almost the same. The results indicated that not only the prediction accuracies, but also the robustness of within-field yield predictions, should be assessed in further studies.
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Chatterjee, Sabyasachi, Swarup Kumar Mondal, Anupam Datta, and Hritik Kumar Gupta. "Enhancing Feature Optimization for Crop Yield Prediction Models." Current Agriculture Research Journal 12, no. 2 (September 10, 2024): 739–49. http://dx.doi.org/10.12944/carj.12.2.19.
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The growth of the world population is leading to an increased demand for food production. Crop yield prediction models are vital for agricultural planning and decision-making, providing forecasts that can significantly impact resource management and food security. This paper focuses on the importance and benefits of feature optimization in enhancing the performance of crop yield prediction models. By reducing noise and complexity, optimized features allow the prediction models to concentrate on the critical factors affecting crop yield, leading to more precise predictions and lesser computation times. This work utilizes an enhanced genetic algorithm to optimize feature selection and model parameters, outperforming the performance of standard genetic algorithms. Comparative analysis showed significant improvement in the accuracy of yield predictions by optimizing the selection of relevant features. The minimal error between actual and predicted yields on both the training and testing datasets highlights the effectiveness of the enhanced genetic algorithm. Enhanced feature optimization not only improves the robustness and adaptability of yield prediction models but also contributes to more sustainable and efficient agricultural management.
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Ulfa, Fathiyya, Thomas G. Orton, Yash P. Dang, and Neal W. Menzies. "Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models." Agronomy 12, no. 2 (February 3, 2022): 384. http://dx.doi.org/10.3390/agronomy12020384.
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One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions.
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Lutman, Peter J. W., Ruth Risiott, and H. Peter Ostermann. "Investigations into Alternative Methods to Predict the Competitive Effects of Weeds on Crop Yields." Weed Science 44, no. 2 (June 1996): 290–97. http://dx.doi.org/10.1017/s0043174500093917.
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Sixteen experiments have investigated alternative methods of predicting the competitive effects of a simulated weed (oats) on the yields of spring barley, spring oilseed rape (canola), peas, spring field (faba) beans and flax. The experiments were designed to discover whether early postemergence assessments of crop and weed vigor would achieve more reliable prediction of yield loss than weed density. Weed density (plants m−2) was a very variable predictor of yield loss. The standardized ranges (range/mean) of values over 3 to 4 years of data for the five crops, in the densities causing 5% yield loss, were between 1.14 and 2.59. Predictions based on the relative dry weight of crop and oats (oat dwt/(oat dwt + crop dwt)), assessed while the plants were still small, achieved more reliable predictions, as the standardized ranges were between 0.10 and 1.86. In three of the experiments, predictions based on relative dry weights were compared to similarly timed predictions based on measurements of relative leaf area and of ground cover, assessed subjectively (by eye) and photographically. Subjective and objective (photographic) assessments of cover achieved similar predictions of yield loss, indicating that visual assessments could be a viable tool to assess the potential competitive effects of weeds.
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Yan, Zhangpeng, Weimin Zhai, and Chao Li. "A novel motherboard test item yield prediction model based on parallel feature extraction." Journal of Physics: Conference Series 2816, no. 1 (August 1, 2024): 012078. http://dx.doi.org/10.1088/1742-6596/2816/1/012078.
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Abstract Functional testing of motherboards in Surface Mount Technology (SMT) assembly lines is crucial. Accurate yield prediction for each test item optimizes testing strategies, reduces costs, and ensures test coverage. Manual estimation of test item yields remains common, hindering accurate on-site predictions. Existing research on motherboard yield lacks predictions for individual test items and ignores temporal correlations during placement. This paper introduces a method, a convolutional bidirectional long short-term memory attention network (CBA-Net), which combines a convolutional neural network and a bidirectional long short-term memory network with an attention mechanism for parallel processing. It preprocesses historical test data, leveraging both networks to identify key features and extract temporal correlations. The attention mechanism optimizes yield predictions by assigning weights to information at different time steps. Experimental validation using actual production data demonstrates that the proposed method performs better compared to traditional models.
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Grzesiak, W., R. Lacroix, J. Wójcik, and P. Blaszczyk. "A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records." Canadian Journal of Animal Science 83, no. 2 (June 1, 2003): 307–10. http://dx.doi.org/10.4141/a02-002.
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Milk yield predictions based on artificial neural etworks and multiple regression were studied. The 305-d lactation yield predictions were based on milk yield of the first 4 test days. Average 305-d milk production of the herd, number of days in milk and month of calving. The predictions made with either the neural network or the multiple regression model did not differ (P > 0.05) from the values estimated with the current Polish dairy cattle evaluation system. The neural network model may be alternative method of predicting these traits. Key words: Artificial neural networks, multiple linear regression, milk yield prediction, test day data
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Vishwajeet Singh, Med Ram Verma, and Subhash Kumar Yadav. "PREDICTIVE MODELLING FOR SUGARCANE PRODUCTION: A COMPREHENSIVE COMPARISON OF ARIMA AND MACHINE LEARNING ALGORITHMS." Applied Biological Research 26, no. 2 (May 30, 2024): 199–209. http://dx.doi.org/10.48165/abr.2024.26.01.23.
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Accurate prediction of sugarcane yield is essential for trade, economic planning, and sustainable agriculture in India. This study addressed the challenge of forecasting sugarcane yield by evaluating the effectiveness of time series modelling and machine learning algorithms. Leveraging data spanning from 2001 to 2020, the research focuses on predicting the sugarcane yield for the subsequent years. The problem statement revolves around the need for precise yield predictions to inform decision-making in the agricultural sector. Methods employed included the utilization of Autoregressive Integrated Moving Average (ARIMA) for time series analysis and machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). The analysis encompassed sugarcane yield data spanning multiple years, with predictions extending for a specified duration. Through analysis of temporal patterns and dependencies within the sugarcane yield time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), the study optimized the predictive models. Results indicated that ARIMA outperformed machine learning algorithms, exhibiting superior performance with a root meansquare error of 36700.68 anda minimumAICvalue of 456.7. The study emphasizes the significance of accurate yield predictions for agricultural planning and decision-making, highlighting the implications for sustainable crop management and the fortification of Indian sugar industry.The study affirms the importance of informed decisions facilitated by accurate yield predictions in resilient agricultural sector. Overall, this study contributes to the advancement of sugarcane yield prediction, offers practical insights for stakeholders and policymakers in India's agricultural landscape.
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Engen, Martin, Erik Sandø, Benjamin Lucas Oscar Sjølander, Simon Arenberg, Rashmi Gupta, and Morten Goodwin. "Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks." Agronomy 11, no. 12 (December 18, 2021): 2576. http://dx.doi.org/10.3390/agronomy11122576.
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Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weather data, farm data, grain delivery data, and cadastre-specific data. We introduce a deep hybrid neural network model to train this multi-temporal data. This model combines the features of convolutional layers and recurrent neural networks to predict farm-scale crop yield production across Norway. The proposed model could efficiently make the target predictions with the mean absolute error of 76 kg per 1000 m2. In conclusion, the reusable farm-scale multi-temporal crop yield dataset and the proposed novel model could meet the actual requirements for the prediction targets in this paper, providing further valuable insights for the research community.
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Semenov, Mikhail A., Rowan A. C. Mitchell, Andrew P. Whitmore, Malcolm J. Hawkesford, Martin A. J. Parry, and Peter R. Shewry. "Shortcomings in wheat yield predictions." Nature Climate Change 2, no. 6 (April 11, 2012): 380–82. http://dx.doi.org/10.1038/nclimate1511.
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Xu, Chang, and Ani L. Katchova. "Predicting Soybean Yield with NDVI Using a Flexible Fourier Transform Model." Journal of Agricultural and Applied Economics 51, no. 3 (May 21, 2019): 402–16. http://dx.doi.org/10.1017/aae.2019.5.
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AbstractWe use models incorporating the normalized difference vegetation index (NDVI) derived from remote sensing satellites to improve soybean yield predictions in 10 major producing states in the United States. Unlike traditional methods that assume an ordinary least squares model applies to all observations, we allow for global flexibility in the relationship between NDVI and soybean yield by using the flexible Fourier transform (FFT) model. FFT results confirm that there is a nonlinear response of soybean yield to NDVI over the growing season. Out-of-sample predictions indicate that allowing for global flexibility with the FFT improves the predictions in time-series prediction and forecasting.
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Son, D. V. "SOIL YIELD FORECASTING." Bulletin of Shakarim University. Technical Sciences 1, no. 4(16) (December 27, 2024): 72–80. https://doi.org/10.53360/2788-7995-2024-4(16)-10.
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This research project serves as a comprehensive meta-analysis in the field of agricultural science, specifically focusing on the prediction of crop yields. This endeavor involves collating and synthesizing findings from a variety of studies and articles that have explored different methodologies and models for forecasting agricultural outputs. The objective of this comprehensive review is to identify trends, methodologies, and key factors that consistently influence crop yield predictions across different studies.It synthesizes methodologies from various studies, emphasizing machine learning (ML) techniques like Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN). These studies integrate high-resolution satellite imagery with environmental indices such as NDVI, EVI, and LAI. Soil chemical properties (pH, nutrients) and satellite-derived data were used to enhance the prediction of crop yields for diverse crops. The findings highlight the comparative effectiveness of different models in handling the spatial and temporal variability of both above-ground and below-ground data, improving prediction accuracy under varying environmental and soil conditions.Through this theoretical analysis, the research underscores the potential of advanced analytical models to transform agricultural monitoring and prediction, providing critical insights that can aid in the optimization of agricultural policies and resource management.
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Yildirim, Tugba, Daniel N. Moriasi, Patrick J. Starks, and Debaditya Chakraborty. "Using Artificial Neural Network (ANN) for Short-Range Prediction of Cotton Yield in Data-Scarce Regions." Agronomy 12, no. 4 (March 29, 2022): 828. http://dx.doi.org/10.3390/agronomy12040828.
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Short-range predictions of crop yield provide valuable insights for agricultural resource management and likely economic impacts associated with low yield. Such predictions are difficult to achieve in regions that lack extensive observational records. Herein, we demonstrate how a number of basic or readily available input data can be used to train an Artificial Neural Network (ANN) model to provide months-ahead predictions of cotton yield for a case study in Menemen Plain, Turkey. We use limited reported yield (13 years) along cumulative precipitation, cumulative heat units, two meteorologically-based drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)), and three remotely-sensed vegetation indices (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI)) as ANN inputs. Results indicate that, when EVI is combined with the preceding 12-month SPEI, it has better sensitivity to cotton yield than other indicators. The ANN model predicted cotton yield four months before harvest with R2 > 0.80, showing potential as a yield prediction tool. We discuss the effects of different combinations of input data (explanatory variables), dataset size, and selection of training data to inform future applications of ANN for early prediction of cotton yield in data-scarce regions.
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Rawat, Meenakshi, Vaishali Sharda, Xiaomao Lin, and Kraig Roozeboom. "Climate Change Impacts on Rainfed Maize Yields in Kansas: Statistical vs. Process-Based Models." Agronomy 13, no. 10 (October 6, 2023): 2571. http://dx.doi.org/10.3390/agronomy13102571.
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The changing climate and the projected increase in the variability and frequency of extreme events make accurate predictions of crop yield critically important for addressing emerging challenges to food security. Accurate and timely crop yield predictions offer invaluable insights to agronomists, producers, and decision-makers. Even without considering climate change, several factors including the environment, management, genetics, and their complex interactions make such predictions formidably challenging. This study introduced a statistical-based multiple linear regression (MLR) model for the forecasting of rainfed maize yields in Kansas. The model’s performance is assessed by comparing its predictions with those generated using the Decision Support System for Agrotechnology Transfer (DSSAT), a process-based model. This evaluated the impact of synthetic climate change scenarios of 1 and 2 °C temperature rises on maize yield predictions. For analysis, 40 years of historic weather, soil, and crop management data were collected and converted to model-compatible formats to simulate and compare maize yield using both models. The MLR model’s predicted yields (r = 0.93) had a stronger association with observed yields than the DSSAT’s simulated yields (r = 0.70). A climate change impact analysis showed that the DSSAT predicted an 8.7% reduction in rainfed maize yield for a 1 °C temperature rise and an 18.3% reduction for a 2 °C rise. The MLR model predicted a nearly 6% reduction in both scenarios. Due to the extreme heat effect, the predicted impacts under uniform climate change scenarios were considerably more severe for the process-based model than for the statistical-based model.
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Schimleck, Laurence R., Peter D. Kube, Carolyn A. Raymond, Anthony J. Michell, and Jim French. "Estimation of whole-tree kraft pulp yield of Eucalyptus nitens using near-infrared spectra collected from increment cores." Canadian Journal of Forest Research 35, no. 12 (December 1, 2005): 2797–805. http://dx.doi.org/10.1139/x05-193.
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Eucalyptus nitens (Deane and Maiden) Maiden (shining gum) is widely grown for kraft pulp production. Improving the kraft pulp yield of E. nitens increases plantation profitability but traditional assessment is slow and expensive, which hinders improvement. Near-infrared (NIR) spectroscopy provides a rapid and inexpensive method for estimating pulp yield, but studies have been limited to estimating whole-tree pulp yield using whole-tree composite samples obtained destructively. For whole-tree pulp-yield calibrations to be used non-destructively they must be applied to increment cores. In this study we used a Tasmanian E. nitens whole-tree pulp yield calibration to estimate the whole-tree pulp yields of trees from a site not included in the calibration. This was done using NIR spectra from increment cores and whole-tree composite chips. Predictions of whole-tree pulp yield based on increment cores were better than those obtained using whole-tree composite chips. The accuracy of pulp-yield predictions was greatly improved by adding a small number of prediction-set samples to the calibration sets. Calibrations for estimating whole-tree pulp yield were also obtained using NIR spectra from milled cores and whole-tree composite chips. The calibrations had similar statistics, indicating that it is possible to obtain calibrations for estimating whole-tree pulp yield based on increment-core NIR spectra.
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Jeschke, Mark R., David E. Stoltenberg, George O. Kegode, Christy L. Sprague, Stevan Z. Knezevic, Shawn M. Hock, and Gregg A. Johnson. "Predicted Soybean Yield Loss As Affected by Emergence Time of Mixed-Species Weed Communities." Weed Science 59, no. 3 (September 2011): 416–23. http://dx.doi.org/10.1614/ws-d-10-00129.1.
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Potential crop yield loss due to early-season weed competition is an important risk associated with postemergence weed management programs. WeedSOFT is a weed management decision support system that has the potential to greatly reduce such risk. Previous research has shown that weed emergence time can greatly affect the accuracy of corn yield loss predictions by WeedSOFT, but our understanding of its predictive accuracy for soybean yield loss as affected by weed emergence time is limited. We conducted experiments at several sites across the Midwestern United States to assess accuracy of WeedSOFT predictions of soybean yield loss associated with mixed-species weed communities established at emergence (VE), cotyledon (VC), first-node (V1), or third-node (V3) soybean. Weed communities across research sites consisted mostly of annual grass species and moderately competitive annual broadleaf species. Soybean yield loss occurred in seven of nine site-years for weed communities established at VE soybean, four site-years for weed communities established at VC soybean, and one site-year for weed communities established at V1 soybean. No soybean yield loss was associated with weed communities established at the V3 stage. Nonlinear regression analyses of predicted and observed soybean yield data pooled over site-years showed that predicted yields were less than observed yields at all soybean growth stages, indicating overestimation of soybean yield loss. Pearson correlation analyses indicated that yield loss functions overestimated the competitive ability of high densities of giant and yellow foxtail with soybean, indicating that adjustments to competitive index values or yield loss function parameters for these species may improve soybean yield loss prediction accuracy and increase the usefulness of WeedSOFT as a weed management decision support system.
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Chen, Yang, Tim R. McVicar, Randall J. Donohue, Nikhil Garg, François Waldner, Noboru Ota, Lingtao Li, and Roger Lawes. "To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction." Remote Sensing 12, no. 10 (May 21, 2020): 1653. http://dx.doi.org/10.3390/rs12101653.
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The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low.
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Meng, Linghua, Huanjun Liu, Susan L. Ustin, and Xinle Zhang. "Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods." Remote Sensing 13, no. 18 (September 19, 2021): 3760. http://dx.doi.org/10.3390/rs13183760.
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Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.
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JHAJHARIA, KAVITA. "Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques." Journal of Agrometeorology 27, no. 1 (March 1, 2025): 63–66. https://doi.org/10.54386/jam.v27i1.2807.
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For global food security, accurate large-scale wheat yield estimates are critical. The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. The results indicated Support Vector Regression outperformed Long Short-Term Machine in wheat yield prediction. In comparison to coarse-resolution SIF products, the high-resolution SIF product offers superior prediction. The results emphasize that with high-quality SIF the crop predictions can be improved.
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Peng, Dailiang, Enhui Cheng, Xuxiang Feng, Jinkang Hu, Zihang Lou, Hongchi Zhang, Bin Zhao, Yulong Lv, Hao Peng, and Bing Zhang. "A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data." Remote Sensing 16, no. 19 (September 27, 2024): 3613. http://dx.doi.org/10.3390/rs16193613.
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Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.
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Linkesh, Monisha, Minakshi Ghorpade, and Pratibha Prasad. "Jowar and Wheat Yield Prediction using a Wavelet based Fusion of Landsat and Sentinel Data with Meteorological Parameters." Indian Journal Of Science And Technology 17, no. 17 (April 14, 2024): 1791–99. http://dx.doi.org/10.17485/ijst/v17i17.413.
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Objectives: The objective of this study is to improve the accuracy of crop yield prediction models, specifically focusing on wheat and jowar crops in Maharashtra during the Rabi season, by integrating Landsat and Sentinel satellite data with meteorological parameters. Methods: The study utilizes Landsat 8 and Sentinel satellite datasets covering Maharashtra State. Atmospheric correction is applied to extract surface properties, followed by wavelet-based fusion to combine the images. Normalized Difference Vegetation Index (NDVI) is calculated and combined with meteorological parameters using ensemble learning techniques, including Random Forest and Ada-Boost algorithms. Comparative analysis is conducted against existing models, considering parameters such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Findings: Significant findings reveal that the proposed methodology outperforms existing models, achieving lower MAE, MSE, and RMSE values for wheat and jowar yield predictions. Additionally, our research highlights the superiority of wheat production over jowar in the Rabi season, based on comprehensive analysis of crop yield predictions. Novelty: This study introduces a novel approach that integrates multiple data sources and employs ensemble learning techniques to enhance crop yield prediction accuracy. By combining Landsat and Sentinel satellite data with meteorological parameters, our methodology provides a more comprehensive understanding of crop growth dynamics, leading to more reliable predictions compared to existing methods. Keywords: Satellite imagery, Machine learning, Normalized Difference Vegetation Index, Fusion, Ensemble learning
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Lou, Zhengfang, Xiaoping Lu, and Siyi Li. "Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning." Agronomy 14, no. 8 (August 20, 2024): 1834. http://dx.doi.org/10.3390/agronomy14081834.
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Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model’s prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture.
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Azizah, Nurul, Sri Suhartini, and Irnia Nurika. "Optimization of Vanillin Extraction from Biodegradation of Oil Palm Empty Fruit Bunches by Serpula lacrymans." Industria: Jurnal Teknologi dan Manajemen Agroindustri 10, no. 1 (April 29, 2021): 33–40. http://dx.doi.org/10.21776/ub.industria.2021.010.01.4.
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Abstract This research aims to determine the combination of the ethyl acetate solvent volume and the extraction time that resulted in the optimum response of vanillin content and vanillin yield from the degradation of lignocellulose components from oil palm empty fruit bunches (OPEFB). First, OPEFB degraded using Serpula lacrymans to break down lignocellulosic components. The research design used a centralized composite design with two factors, the volume of ethyl acetate solvent (ml) and the extraction time (minutes). The responses of the experiment are vanillin content and vanillin yields. The optimization analysis results showed that the volume of ethyl acetate solvent and extraction time have a quadratic effect on the vanillin content and vanillin yields. The optimal solution was obtained by treatment with ethyl acetate volume 101.1 ml and extraction time 123.5 minutes. The optimal solution prediction results obtained vanillin content 0.014% and vanillin yield 7.302 μg/g with desirability of 92.8%. Validation based on the optimal solution’s prediction brought response vanillin content 0.013% and vanillin yield 6.950 μg/g. The vanillin content and yield validation results differed respectively by 4.081% and 4.826% lower when compared to predictions on the optimal solution. Keywords: ethyl acetate, vanillin content, vanillin yield
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Sadenova, Marzhan, Nail Beisekenov, Petar Sabev Varbanov, and Ting Pan. "Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan." Agriculture 13, no. 6 (June 3, 2023): 1195. http://dx.doi.org/10.3390/agriculture13061195.
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The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017–2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.
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Kostyra, Tomasz Piotr. "Forecasting the yield curve for Poland with the PCA and machine learning." Bank i Kredyt Vol. 55, No. 4 (August 31, 2024): 459–78. http://dx.doi.org/10.5604/01.3001.0054.8580.
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The article examines the application of the Principal Component Analysis (PCA) and machine learning method, the Long Short-Term Memory (LSTM), in the prediction of the yield curve for Poland. The PCA was applied to decompose the yield curve, forecast its components using the LSTM, and obtain the yield curve predictions upon recomposition. The results from the PCA-LSTM model were compared to predictions generated directly by the LSTM model, simple autoregression and random walk, which serves as a benchmark. Overall, LSTM predictions are the most accurate with PCA-LSTM being a close second, nonetheless PCA-LSTM is more accurate in short-term forecasting of interest rates at long maturities. Both methods outperform the benchmark, while autoregression usually underperforms. For these reasons, the PCA-LSTM as well as the LSTM can be useful in interest rate management or building trading strategies. The PCA-LSTM has the advantage that it can focus on particular components of the yield curve, such as variability of the yield curve’s level or steepness.
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Pravallika, K., G. Karuna, K. Anuradha, and V. Srilakshmi. "Deep Neural Network Model for Proficient Crop Yield Prediction." E3S Web of Conferences 309 (2021): 01031. http://dx.doi.org/10.1051/e3sconf/202130901031.
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Crop yield forecasting mainly focus on the domain of agriculture research which has a great impact on making decisions like import-export, pricing and distribution of respective crops. Accurate predictions with well timed forecasts is very important and is a tremendously challenging task due to numerous complex factors. Mainly crops like wheat, rice, peas, pulses, sugarcane, tea, cotton, green houses etc. can be used for crop yield prediction. Climatic changes and unpredictability influence mainly on crop production and maintenance. Forecasting crop yield well before harvest time can help farmers for selling and storage. Agriculture deals with large datasets and knowledge process. Many techniques are there to predict the crop yield. Farmers are benefited commercially by these predictions. Factors such as Geno type, Environment, Climatic conditions and Soil types used in predicting the Yield. For predicting accurately we need to know the fundamental understanding and relationship between the interactive factors and the yield to reveal the relationships between the datasets which are comprehensive and powerful algorithms. Based on the study of various survey papers it has been found that in all the crop predictions, various deep learning, machine learning and ANN algorithms implemented to predict yield forecast and the results are analyzed.
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Barton, N., P. Dawson, and M. Miller. "Yield Strength Asymmetry Predictions From Polycrystal Elastoplasticity." Journal of Engineering Materials and Technology 121, no. 2 (April 1, 1999): 230–39. http://dx.doi.org/10.1115/1.2812370.
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Since the 1960s, it has been known that elastoplastic polycrystal models predict asymmetries in the yield strength for polycrystals that have been prestrained. After prestraining in tension, a model polycrystal exhibits Bauschinger-like behavior in that it yields in compression at a lower stress magnitude than in tension. Furthermore, the knee of the reloading stress-strain curve is more gradual for compression than for tension. The origins of these behaviors reside in the assumption that links the macroscopic deformation to the deformations in individual crystals. More precisely, the reloading response is biased by the residual stress field which is induced with plastic straining by the anisotropy of the single crystal yield surface. While the earlier work pointed to the polycrystalline origins of the asymmetry, it did not resolve the degree to which the particular linking assumption affects the amount of asymmetry. However, due to the strong influence of the linking assumption on the crystal stresses, the sensitivity of the asymmetry to the linking assumption is expected to be appreciable. In this paper we examine the influence of the linking assumption on the magnitude of the computed yield strength asymmetry of prestrained polycrystals. Elastoplastic polycrystal simulations based on upper bound (Taylor) and lower bound (equilibrium-based) linking assumptions are compared to finite element computations in which elements constitute individual crystals. The finite element model maintains compatibility while satisfying equilibrium in a weak sense and treats the influence of neighboring crystals explicitly. The strength of the predicted Bauschinger effect does depend on the linking assumption, with ‘compatibility first’ models developing stronger yield strength asymmetries.
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Ayu Siregar, Silviana, and Yusuf Ramadhan Nasution. "Prediction of Rice Farming Yields in Padangsidimpuan City through Support Vector Machine (SVM) Algorithms." JINAV: Journal of Information and Visualization 5, no. 1 (August 10, 2024): 146–56. https://doi.org/10.35877/454ri.jinav2876.
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The purpose of this study is to determine the prediction of rice farming yields in Padangsidimpuan City through SVM (Support Vector Machine) Algorithms. This type of research used quantitative methods of SVM (Support Vector Machine) with a Data-Driven development (DDD) method. This approach utilized patterns and trends in data to build accurate prediction models where the DDD method can be used when researchers have access to relevant and meaningful data to guide the development of software or prediction models.The SVM algorithm has proven to be effective in predicting rice yield trends, both in determining the direction of change (up or down) and in estimating the value of the next harvest. The implemented SVM model is able to identify patterns of change in historical data and provide relevant predictions for agricultural yields. Historical data covering a fairly long period of time provides sufficient information for models to identify trends and patterns. This model can provide better predictions with more complete and high-quality data.
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Feng, Yu, Wen Lin, Shaobo Yu, Aixia Ren, Qiang Wang, Hafeez Noor, Jianfu Xue, Zhenping Yang, Min Sun, and Zhiqiang Gao. "Effects of fallow tillage on winter wheat yield and predictions under different precipitation types." PeerJ 9 (December 8, 2021): e12602. http://dx.doi.org/10.7717/peerj.12602.
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In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.
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Xavier, Alencar, and Katy M. Rainey. "Quantitative Genomic Dissection of Soybean Yield Components." G3: Genes|Genomes|Genetics 10, no. 2 (December 9, 2019): 665–75. http://dx.doi.org/10.1534/g3.119.400896.
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Soybean is a crop of major economic importance with low rates of genetic gains for grain yield compared to other field crops. A deeper understanding of the genetic architecture of yield components may enable better ways to tackle the breeding challenges. Key yield components include the total number of pods, nodes and the ratio pods per node. We evaluated the SoyNAM population, containing approximately 5600 lines from 40 biparental families that share a common parent, in 6 environments distributed across 3 years. The study indicates that the yield components under evaluation have low heritability, a reasonable amount of epistatic control, and partially oligogenic architecture: 18 quantitative trait loci were identified across the three yield components using multi-approach signal detection. Genetic correlation between yield and yield components was highly variable from family-to-family, ranging from -0.2 to 0.5. The genotype-by-environment correlation of yield components ranged from -0.1 to 0.4 within families. The number of pods can be utilized for indirect selection of yield. The selection of soybean for enhanced yield components can be successfully performed via genomic prediction, but the challenging data collections necessary to recalibrate models over time makes the introgression of QTL a potentially more feasible breeding strategy. The genomic prediction of yield components was relatively accurate across families, but less accurate predictions were obtained from within family predictions and predicting families not observed included in the calibration set.
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Huber, Florian, Alvin Inderka, and Volker Steinhage. "Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning." Sensors 24, no. 3 (January 24, 2024): 770. http://dx.doi.org/10.3390/s24030770.
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Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.
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Pazhanivelan, Sellaperumal, N. S. Sudarmanian, S. Satheesh, and K. P. Ragunath. "Innovative Approaches to Bengal gram Yield Mapping: Integration of Sentinel-1 SAR and Crop Simulation Models for Precision Agriculture." Journal of Scientific Research and Reports 31, no. 1 (January 25, 2025): 449–60. https://doi.org/10.9734/jsrr/2025/v31i12788.
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Accurate spatial yield estimation is crucial for optimizing agricultural management and ensuring food security. This study integrates Sentinel-1A SAR remote sensing data and the DSSAT crop simulation model to predict Bengal gram (chickpea) yield in Nagaur district, Rajasthan, India. Sentinel-1A backscatter data were processed for crop area mapping, achieving an overall classification accuracy of 85.1% and a kappa index of 0.70, demonstrating the reliability of SAR for agricultural monitoring under diverse weather conditions. Leaf Area Index (LAI) was derived from SAR backscatter values and linked to DSSAT-simulated yields, generating spatial yield predictions. Validation using Crop Cutting Experiment (CCE) data showed a high agreement of 91.3% between predicted and observed yields, with low root mean square error (RMSE), confirming model accuracy. This research highlights the synergistic potential of SAR-based remote sensing and simulation models for large-scale yield forecasting, advancing precision agriculture. Future efforts may incorporate additional sensors and machine learning to further enhance prediction accuracy and adaptability to climate variability.
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Gupta, Soma, Satarupa Mohanty, and Dayal Kumar Behera. "AI-based Yield Prediction: A Thorough Review." Indian Journal Of Science And Technology 18, no. 10 (March 16, 2025): 822–38. https://doi.org/10.17485/ijst/v18i10.175.
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Background: Traditional farming practices often rely on conventional methods and anecdotal knowledge, but accurate crop yield prediction is crucial for strategic decision-making in agriculture, including import-export strategies and financial planning. Machine learning, a subset of artificial intelligence, offers a data-driven approach that can improve yield prediction by considering multiple factors. ML models can be either explanatory, analyzing past events, or predictive, forecasting future outcomes. Effective feature selection and data preprocessing are essential for accurate ML-based yield prediction. Objectives: The primary objective of this review is to analyze the existing research to outline the crucial components used for crop yield prediction techniques. The review focuses on comparative and thorough assessment of the crop yield predictions methods and feature sets used. It examines the benefits and challenges of using various algorithms and features for yield prediction Methods: The study employed a systematic literature review methodology. A comprehensive search query was used across five databases, initially retrieving 450 articles. After applying inclusion and exclusion criteria, 40 articles were selected for in-depth review. The selected studies were analyzed to identify the ML and DL algorithms used, the features employed, and the overall findings. Findings: This research introduces several novel aspects to the field of crop yield prediction. The review revealed that various ML models, including Decision Trees, Random Forests, Support Vector Machines, Bayesian Networks, and Artificial Neural Networks, are used for crop yield prediction. Within DL, Convolutional Neural Networks, Long Short-Term Memory networks, and Deep Neural Networks were identified as frequently used algorithms. The review also highlights the importance of feature selection techniques in preprocessing raw data. It investigates combining information from diverse sources like images, text, and sensor readings to create richer representations for yield prediction. By addressing these directions, future research can contribute to more sustainable, resilient, and productive agricultural systems, enhancing global food security in the face of growing challenges. Keywords: Yield prediction, Feature Selection, Machine Learning (ML), Deep Learning (DL)
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Erik, E., M. Durmaz, and A. Ö. Ok. "IN AND END OF SEASON SOYBEAN YIELD PREDICTION WITH HISTOGRAM BASED DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 95–100. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-95-2023.
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Abstract. One sector that feels the effects of global warming and climate change on all levels is agriculture. In order to prepare for possible yield loss, as well as market, storage, and import planning challenges brought on by climate change, businesses can utilise agricultural decision support applications. Within the scope of this study, a crop yield prediction module has been developed that can provide in and end of season estimation of crop yields to be obtained from the determined regions. The Python programming language was used in the creation of the module as a QGIS plugin. The area for which crop yield predictions are to be made is covered by retrieving MODIS SR, MODIS LST, and Daymet data from the Google Earth Engine data catalogue. Histograms obtained from remotely sensed images are used as input data to two deep learning methods (CNN-LSTM and HistCNN). As a result, the HistCNN model outperformed CNN-LSTM for in season soybean yield prediction, with an R2 of 0.72, while the CNN-LSTM model outperformed it for in end of season soybean yield prediction, with an R2 of 0.67.
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Kalpana, P., I. Anusha Prem, S. Josephine Reena Mary, and ArockiaValan Rani. "Crop Yield Prediction Using Machine Learning." REST Journal on Data Analytics and Artificial Intelligence 2, no. 1 (March 1, 2023): 16–20. http://dx.doi.org/10.46632/jdaai/2/1/3.
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The majority of India’s agricultural products have been negatively impacted by climate change in terms of performance over the past 20 years. Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on the agricultural field, this project will assist the farmers in learning the yield of their crop, enabling them to make the best choices. By creating a working prototype of an interactive prediction system, it tries to find a solution. It will be put into practise to implement such a system with a user-friendly web-based graphic user interface and the machine learning algorithm. The farmer will have access to the prediction’s outcomes. So, there are various ways or algorithms for this type of data analytics in crop prediction, and we can anticipate crop production with the aid of those algorithms. It employs the random forest algorithm. There are no suitable technologies or solutions to deal with the scenario we are in, despite the analysis of all these concerns and problems, including weather, temperature, humidity, rainfall, and moisture. In India, there are numerous ways to boost agricultural economic development. Data mining can be used to forecast crop yield growth. Data mining is, in general, the process of reviewing data from various angles and distilling it into pertinent information. The most well-known and effective supervised machine learning algorithm, random forest, can perform both classification and regression tasks. It works by building a large number of decision trees during training time and producing output of the class that is the mean prediction (for regression) or mode of the classes (for classification) of the individual trees.
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Kalpana, P., I. Anusha Prem, S. Josephine Reena Mary, and ArockiaValan Rani. "Crop Yield Prediction Using Machine Learning." REST Journal on Data Analytics and Artificial Intelligence 2, no. 1 (March 1, 2023): 16–20. http://dx.doi.org/10.46632/10.46632/jdaai/2/1/3.
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The majority of India’s agricultural products have been negatively impacted by climate change in terms of performance over the past 20 years. Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on the agricultural field, this project will assist the farmers in learning the yield of their crop, enabling them to make the best choices. By creating a working prototype of an interactive prediction system, it tries to find a solution. It will be put into practise to implement such a system with a user-friendly web-based graphic user interface and the machine learning algorithm. The farmer will have access to the prediction’s outcomes. So, there are various ways or algorithms for this type of data analytics in crop prediction, and we can anticipate crop production with the aid of those algorithms. It employs the random forest algorithm. There are no suitable technologies or solutions to deal with the scenario we are in, despite the analysis of all these concerns and problems, including weather, temperature, humidity, rainfall, and moisture. In India, there are numerous ways to boost agricultural economic development. Data mining can be used to forecast crop yield growth. Data mining is, in general, the process of reviewing data from various angles and distilling it into pertinent information. The most well-known and effective supervised machine learning algorithm, random forest, can perform both classification and regression tasks. It works by building a large number of decision trees during training time and producing output of the class that is the mean prediction (for regression) or mode of the classes (for classification) of the individual trees.
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Nagesh, V. "CROP RECOMMENDATION SYSTEM USING KNN ALGORITHM AND RANDOM FOREST." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27660.
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In agriculture, the integration of machine learning has been a long-standing aspiration, resulting in significant advancements. While machine learning models have been developed for crop and yield predictions, traditional algorithms like decision trees often fall short of delivering the desired accuracy. This paper introduces an accessible and user-friendly solution for crop recommendations and yield predictions. Users provide inputs such as temperature, humidity, soil pH, and rainfall. To enhance accuracy, a hybrid approach using K-nearest neighbor (KNN) and Random Forest (RF) algorithms is employed. The K-nearest neighbor (KNN) algorithm achieves an impressive accuracy rate of 98%. Additionally, the Random Forest (RF) algorithm attains a commendable 96% accuracy by aggregating multiple decision trees. These high accuracy rates signify the system's potential to empower farmers with data-driven insights for crop selection and yield projections. Furthermore, the user-friendly interface promises broader adoption within the agriculture sector, catering to users with varying levels of technical proficiency. To strengthen the system's credibility, transparency regarding data sources and quality is imperative. Utilizing accurate and relevant data for reliable predictions. In summary, this paper presents a promising solution for informed decision-making in agriculture, combining crop recommendations and yield predictions. Acknowledging the limitations of traditional approaches, it capitalizes on the strengths of K-nearest neighbor and Random Forest algorithms. Keywords: Crop recommendation, Yield prediction, Machine learning, KNN, Random Forest
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Hock, Shawn M., Stevan Z. Knezevic, William G. Johnson, Christy Sprague, and Alex R. Martin. "WeedSOFT: Effects of Corn-Row Spacing for Predicting Herbicide Efficacy on Selected Weed Species." Weed Technology 21, no. 1 (March 2007): 219–24. http://dx.doi.org/10.1614/wt-06-008.1.
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The ability to accurately estimate herbicide efficacy is critical for any decision-support system used in weed management. Recent efforts by weed scientists in the North Central United States to adopt WeedSOFT across a broad region have resulted in a number of regional research projects designed to assess and improve the predictive capability of WeedSOFT. Field studies were conducted from 2000 to 2002 in Nebraska, Missouri, and Illinois to evaluate herbicide-efficacy predictions made by WeedSOFT in two corn-row spacings. Following crop and weed emergence, input variables, such as weed densities and heights, were entered into WeedSOFT to generate a list of treatments ranked by predicted crop yields. The five treatments evaluated included those predicting highest crop-yield potential (recommended control treatment 1), a 10% yield reduction, a 20% yield reduction, a 10% yield reduction plus cultivation, and cultivation alone. These treatments were applied to corn grown in 38- and 76-cm rows. Generally, treatments applied in 38-cm rows had more accurate herbicide-efficacy predictions compared with 76-cm rows. WeedSOFT provided better control predictions for broadleaf than grass species. WeedSOFT provided excellent herbicide-efficacy predictions for the highest crop-yield potential, which indicates a good potential for practical use of this software for herbicide recommendations.
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Sreekanth, S. "HarvestMax: A Predictive Model for Crop Yield and Fertilizer Optimization." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 2841–47. http://dx.doi.org/10.22214/ijraset.2024.60339.
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Abstract: In the backdrop of India's agrarian-centric economy, the precision of crop yield prediction and the adoption of optimal farming practices emerge as critical components for sustainable agricultural development. This research paper introduces an innovative methodology that integrates data mining techniques, machine learning algorithms such as Support Vector Machines (SVM), and Regression algorithms, along with a comprehensive attribute analysis to establish a resilient crop yield prediction and recommendation system. The proposed system draws insights from a diverse range of attributes crucial for agriculture, including geographical location, soil pH for alkalinity assessment, nutrient percentages (Nitrogen, Phosphorous, and Potassium), real-time weather conditions sourced from third-party APIs, soil type, nutrient composition, and regional rainfall data. By amalgamating and scrutinizing these multifaceted attributes, our system aspires to furnish farmers with precise and dependable predictions regarding their crop yields.
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Bi, Hele, Jiale Jiang, Junzhao Chen, Xiaojun Kuang, and Jinxiao Zhang. "Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules." Materials 17, no. 7 (April 4, 2024): 1664. http://dx.doi.org/10.3390/ma17071664.
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The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials.
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Karthikeyan, R., M. Gowthami, A. Abhishhek, and P. Karthikeyan. "Implementation of Effective Crop Selection by Using the Random Forest Algorithm." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 287. http://dx.doi.org/10.14419/ijet.v7i3.34.19209.
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Accurate predictions of crop yield are critical for developing agriculture. We have provided a machine-learning method, Random Forests which has a ability to predict crop yield corresponds to the current climate and biophysical change. We have collected a large crop yield data from various sources. These data are used for both for the model training and testing.RF was found huge capable of predicting crop yields and over performed MLR standards in every performance statistics that were compared. From various results that shows that RF is an efficient machine-learning algorithm for crop predictions at current condition and has a huge accuracy in data analysis.
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Cao, Junjun, Huijing Wang, Jinxiao Li, Qun Tian, and Dev Niyogi. "Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction." Remote Sensing 14, no. 7 (April 1, 2022): 1707. http://dx.doi.org/10.3390/rs14071707.
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Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R2 of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.
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HUNDAL, S. S., and PRABHJYOT-KAUR. "Application of the CERES–Wheat model to yield predictions in the irrigated plains of the Indian Punjab." Journal of Agricultural Science 129, no. 1 (August 1997): 13–18. http://dx.doi.org/10.1017/s0021859697004462.
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The crop–environment resource synthesis model for wheat, CERES–Wheat, was used to simulate yields from 1985 to 1993 at Ludhiana, India. The simulated anthesis and physiological maturity dates, grain and total biomass yields of wheat were compared with actual observations for the commonly grown cultivar, HD–2329. The simulated and actual dates of phenological events showed deviations from only −9 to +6 days for anthesis and −6 to +3 days for physiological maturity of the crop. The model estimated the kernel weight within 88–113% (mean 100%) of the actual kernel weights. The model predicted the grain yields from 80 to 115% (mean 97·5%) of the observed grain yield. Biomass yields were predicted from 93 to 128% (mean 110·5%) of the observed yields. The results obtained with the model for the eight crop seasons demonstrated satisfactory predictions of phenology, growth and yield of wheat. However, the biomass simulations indicated the need for further examination of the factors controlling the partitioning of photosynthates during crop growth. The results of this study reveal that the calibrated CERES–Wheat model can be used for the prediction of wheat growth and yield in the central irrigated plains of the Indian Punjab.
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Orduna-Cabrera, Fernando, Alejandro Rios-Ochoa, Federico Frank, Soeren Lindner, Marcial Sandoval-Gastelum, Michael Obersteiner, and Valeria Javalera-Rincon. "Short-Term Forecasting Arabica Coffee Cherry Yields by Seq2Seq over LSTM for Smallholder Farmers." Sustainability 17, no. 9 (April 25, 2025): 3888. https://doi.org/10.3390/su17093888.
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Coffee production is a vital source of income for smallholder farmers in Mexico’s Chiapas, Oaxaca, Puebla, and Veracruz regions. However, climate change, fluctuating yields, and the lack of decision-support tools pose challenges to the implementation of sustainable agricultural practices. The SABERES project aims to address these challenges through a Seq2Seq-LSTM model for predicting coffee yields in the short term, using datasets from Mexican national institutions, including the Agricultural Census (SIAP) and environmental data from the National Water Commission (CONAGUA). The model has demonstrated high accuracy in replicating historical yields for Chiapas and can forecast yields for the next two years. As a first step, we assessed coffee yield prediction for Bali, Indonesia, by comparing the LSTM, ARIMA, and Seq2Seq-LSTM models using historical data. The results show that the Seq2Seq-LSTM model provided the most accurate predictions, outperforming LSTM and ARIMA. Optimal performance was achieved using the maximum data sequence. Building on these findings, we aimed to apply the best configuration to forecast coffee yields in Chiapas, Mexico. The Seq2Seq-LSTM model achieved an average difference of only 0.000247, indicating near-perfect accuracy. It, therefore, demonstrated high accuracy in replicating historical yields for Chiapas, providing confidence for the next two years’ predictions. These results highlight the potential of Seq2Seq-LSTM to improve yield forecasts, support decision making, and enhance resilience in coffee production under climate change.
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Kacar, Ilyas, Fahrettin Ozturk, Serkan Toros, and Suleyman Kilic. "Prediction of Strain Limits via the Marciniak-Kuczynski Model and a Novel Semi-Empirical Forming Limit Diagram Model for Dual-Phase DP600 Advanced High Strength Steel." Strojniški vestnik – Journal of Mechanical Engineering 66, no. 10 (October 15, 2020): 602–12. http://dx.doi.org/10.5545/sv-jme.2020.6755.
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The prediction capability of a forming limiting diagram (FLD) depends on how the yield strength and anisotropy coefficients evolve during the plastic deformation of sheet metals. The FLD predictions are carried out via the Marciniak-Kuczynski (M-K) criterion with anisotropic yield functions for DP600 steel of various thicknesses. Then, a novel semi-empirical FLD criterion is proposed, and prediction capabilities of the criterion are tested with different yield criteria. The results show that the yield functions are very sensitive to anisotropic evolution. Thus, while the FLD curves from the M-K model and the proposed model are not the same for each thickness, the proposed model has better prediction than the M-K model.
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Ziliani, M. G., M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe. "INTRA-FIELD CROP YIELD VARIABILITY BY ASSIMILATING CUBESAT LAI IN THE APSIM CROP MODEL." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 1045–52. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1045-2022.
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Abstract. Predicting within-field crop yield early in the season can help address crop production challenges to improve farmers’ economic return. While yield prediction with remote sensing has been a research aim for years, it is only recently that observations with the suited spatial and temporal resolutions have become accessible to improve crop yield predictions.Here we developed a yield prediction framework that integrates daily high-resolution (3 m) CubeSat imagery into the APSIM crop model. The approach trains a regression model that correlates simulated yield to simulated leaf area index (LAI) from APSIM. That relationship is then employed to determine the optimum date at which the regression best predicts yield from the LAI. Additionally, our approach can forecast crop yield by utilizing a particle filter to assimilate CubeSat-based LAI in the model APSIM to generate yield maps at 3 m several weeks before the optimum regression date. Our method was evaluated for a rainfed site located in the US Corn belt, using a collection of spatially varying yield data. The proposed approach does not need in situ data to rain the regression, with outcomes reporting that even with a single assimilation step, accurate yield predictions were provided up to 21 days before the optimum regression date. The spatial variability of crop yield was reproduced fairly well, with a good correlation against in situ measurements (R2 = 0.73 and RMSE = 1.69), demonstrating that high-resolution yield predictions early in the season have great potential to meet and improve upon digital agricultural goals.
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Spejewski, E. H., H. K. Carter, B. Mervin, E. Prettyman, A. Kronenberg, and D. W. Stracener. "ISOL yield predictions from holdup-time measurements." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 266, no. 19-20 (October 2008): 4271–74. http://dx.doi.org/10.1016/j.nimb.2008.05.048.
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Hara, Patryk, Magdalena Piekutowska, and Gniewko Niedbała. "Prediction of Pea (Pisum sativum L.) Seeds Yield Using Artificial Neural Networks." Agriculture 13, no. 3 (March 12, 2023): 661. http://dx.doi.org/10.3390/agriculture13030661.
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A sufficiently early and accurate prediction can help to steer crop yields more consciously, resulting in food security, especially with an expanding world population. Additionally, prediction related to the possibility of reducing agricultural chemistry is very important in an era of climate change. This study analyzes the performance of pea (Pisum sativum L.) seed yield prediction by a linear (MLR) and non-linear (ANN) model. The study used meteorological, agronomic and phytophysical data from 2016–2020. The neural model (N2) generated highly accurate predictions of pea seed yield—the correlation coefficient was 0.936, and the RMS and MAPE errors were 0.443 and 7.976, respectively. The model significantly outperformed the multiple linear regression model (RS2), which had an RMS error of 6.401 and an MAPE error of 148.585. The sensitivity analysis carried out for the neural network showed that the characteristics with the greatest influence on the yield of pea seeds were the date of onset of maturity, the date of harvest, the total amount of rainfall and the mean air temperature.
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Hina, Firdous, and Dr Mohd Tahseenul Hasan. "Agriculture Crop Yield Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 910–15. http://dx.doi.org/10.22214/ijraset.2022.41381.
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Abstract: In our suggested system, we employed a vast dataset that included all of India's states, whereas in the old system, just a single state was considered. These suggestions may be extracted and used to educate the farmers. The farmer can have a better understanding of the crops to cultivate by using a pictorial depiction. Machine Learning Techniques develops a well-defined model with the data and helps us to attain predictions. Agricultural issues like crop prediction, rotation, water requirement, fertilizer requirement and protection can be solved. Due to the variable climatic factors of the environment, there is a necessity to have a efficient technique to facilitate the crop cultivation and to lend a hand to the farmers in their production and management. This may help upcoming agriculturalists to have a better agriculture. A system of recommendations can be provided to a farmer to help them in crop cultivation with the help of data mining. To implement such an approach, crops are recommended based on its climatic factors and quantity. Data Analytics paves a way to evolve useful extraction from agricultural database. Crop Dataset has been analyzed and recommendation of crops is done based on productivity and season Keywords: Machine learning, Agriculture techniques, crop predictions
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Sharma, Suresh Kumar, Durga Prasad Sharma, and Kiran Gaur. "Machine Learning Techniques for Crop Yield Forecasting in Semi-Arid (3A) Zone, Rajasthan (India)." Current Agriculture Research Journal 11, no. 3 (January 5, 2024): 895–914. http://dx.doi.org/10.12944/carj.11.3.19.
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Economic growth and prosperity of a nation are inextricably linked to the agricultural sector. In the compass of agriculture, climate and other environmental changes are one of the main challenges. The present study attempts to predict crop yield for the Jaipur district which is an important region in the semi-arid eastern plain of Rajasthan (India). Machine learning (ML) techniques are used in forecasting and developing practical solutions for numerous challenges such as climate change with other environmental factors. Crop yield prediction is the process of predicting yield using historical data through meteorological parameters and past yield records. This paper used the agrometeorological time-series data from the year 1991 to 2020 for optimal yield forecasting. There have been numerous attempts to improve crop yield prediction by employing machine learning techniques. However, in this study, fusing the intelligence of reinforcement with deep learning, we got a comprehensive framework for mapping raw data to crop prediction values, allowing an optimal estimation of crop yields with higher accuracy. Upon comparative analysis of numerous ML algorithms, Random Forest is found the best-performing algorithm with an accuracy of 92.3% using supervised machine learning methods. With an accuracy of 92.3%, the proposed Random Forest-based model outperforms other techniques that are currently being used to predict crop yields. The study predictions could significantly help in choosing the best cropping pattern and planning for action accordingly. The results provide the best ways to solve environmental and agricultural problems in this semi-arid region of the specified Rajasthan state facing climate change issues.