Relevant bibliographies by topics / Satellite crop prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Satellite crop prediction'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Satellite crop prediction"

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Ankit Raj Goyal and Nikkisha Subramaniam. "Satellite Crop Monitoring and Farm Yield Prediction - A Review and Future Prospects." Acceleron Aerospace Journal 4, no. 5 (2025): 1150–55. https://doi.org/10.61359/11.2106-2529.

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Satellite imaging of crops provides valuable insights into crop health, stress, and yield to optimize production and boost yields. Satellite crop monitoring data is richer and more efficient than traditional manual and ground-based methods. Satellite based farm monitoring and planning has thus been implemented since the 1970s using satellites like Landsat, Sentinel and Resourcesat. This study presents a review of satellite remote sensing technology used in crop monitoring and farm yield prediction. We take an in-depth look at the famous satellites used for this purpose. Satellite based methods
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Jhajharia, Kavita, Neha V. Sharma, and Pratistha Mathur. "A Machine Learning Model for Crop Yield Prediction Using Remote Sensing Data." International Research Journal of Multidisciplinary Scope 06, no. 02 (2025): 577–90. https://doi.org/10.47857/irjms.2025.v06i02.03182.

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Precisely estimating crop yields is a critical aspect of agricultural planning, resource allocation, and food security. Satellite data integrated with machine learning algorithms have recently become a potential solution for predicting crop yield at local and global levels. The present study provides detailed investigation of satellite-based crop yield prediction using machine-learning algorithms. The proposed methodology integrates satellite imagery data with precipitation data. We use machine learning algorithms for predictive modelling, random forests, support vector machines, decision tree
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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 (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 yiel
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Chen, Jiang, Tong Yu, Jerome H. Cherney, and Zhou Zhang. "Optimal Integration of Optical and SAR Data for Improving Alfalfa Yield and Quality Traits Prediction: New Insights into Satellite-Based Forage Crop Monitoring." Remote Sensing 16, no. 5 (2024): 734. http://dx.doi.org/10.3390/rs16050734.

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Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sent
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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 (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 en
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Kumhálová, Jitka, and Štěpánka Matějková. "Yield variability prediction by remote sensing sensors with different spatial resolution." International Agrophysics 31, no. 2 (2017): 195–202. http://dx.doi.org/10.1515/intag-2016-0046.

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Abstract Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Ver
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Hsiou, Dong-Chong, Fay Huang, Fu Jie Tey, Tin-Yu Wu, and Yi-Chuan Lee. "An Automated Crop Growth Detection Method Using Satellite Imagery Data." Agriculture 12, no. 4 (2022): 504. http://dx.doi.org/10.3390/agriculture12040504.

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This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite imag
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Monisha, Linkesh, Ghorpade Minakshi, and Prasad Pratibha. "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 (2024): 1791–99. https://doi.org/10.17485/IJST/v17i17.413.

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Abstract <strong>Objectives:</strong>&nbsp;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.&nbsp;<strong>Methods:</strong>&nbsp;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 calcu
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Chiu, Marco Spencer, and Jinfei Wang. "Local Field-Scale Winter Wheat Yield Prediction Using VENµS Satellite Imagery and Machine Learning Techniques." Remote Sensing 16, no. 17 (2024): 3132. http://dx.doi.org/10.3390/rs16173132.

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Reliable and accurate crop yield prediction at the field scale is critical for meeting the global demand for reliable food sources. In this study, we tested the viability of VENμS satellite data as an alternative to other popular and publicly available multispectral satellite data to predict winter wheat yield and produce a yield prediction map for a field located in southwestern Ontario, Canada, in 2020. Random forest (RF) and support vector regression (SVR) were the two machine learning techniques employed. Our results indicate that machine learning models paired with vegetation indices (VIs
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Son, D. V. "SOIL YIELD FORECASTING." Bulletin of Shakarim University. Technical Sciences 1, no. 4(16) (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 (
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Dissertations / Theses on the topic "Satellite crop prediction"

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(5930423), Min Xu. "Using Digital Agriculture Methodologies to Generate Spatial and Temporal Predictions of N Conservation, Management and Maize Yield." Thesis, 2019.

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<div>The demand for customized farm management prescription is increasing in order to maximize crop yield and minimize environmental risks under a changing climate. One great challenge to modeling crop growth and production is spatial and temporal variability. The goal of this dissertation research is to use publicly available Landsat imagery, ground samples and historical yield data to establish methodologies to spatially quantify cover crop growth and in-season maize yield. First, an investigation was conducted into the feasibility of using satellite remote sensing and spatial interpolation
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Book chapters on the topic "Satellite crop prediction"

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Kumar, Sunil, Shashi Mesapam, and Allu Pavan Kumar Reddy. "Crop Phenology Mapping and Crop Yield Prediction Using Satellite Images." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7467-8_26.

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Divakar, M. Sarith, M. Sudheep Elayidom, and R. Rajesh. "Sequence Models for Crop Yield Prediction Using Satellite Imagery." In Computational Vision and Bio-Inspired Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9573-5_49.

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Gore, Santosh, Dipak Patil, Nitin Mahankale, and Sujata Gore. "Satellite Imaging for Precision Agriculture Enhancing Crop Management, Soil Condition and Yield Prediction." In Emerging Trends in Smart Societies. Routledge, 2024. http://dx.doi.org/10.4324/9781003489412-104.

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Basist, Alan, Ariel Dinar, Brian Blankespoor, David Bachiochi, and Harold Houba. "Use of Satellite Information on Wetness and Temperature for Crop Yield Prediction and River Resource Planning." In Climate Smart Agriculture. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61194-5_5.

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Sarith Divakar, M., M. Sudheep Elayidom, and R. Rajesh. "Feature Engineering of Remote Sensing Satellite Imagery Using Principal Component Analysis for Efficient Crop Yield Prediction." In Evolutionary Computing and Mobile Sustainable Networks. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9605-3_13.

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Habyarimana, Ephrem. "Future Vision, Summary and Outlook." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_21.

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AbstractThe DataBio’s agriculture pilots were carried out through a multi-actor whole-farm management approach using information technology, satellite positioning and remote sensing data as well as Internet of Things technology. The goal was to optimize the returns on inputs while reducing environmental impacts and streamlining the CAP monitoring. Novel knowledge was delivered for a more sustainable agriculture in line with the FAO call to achieve global food security and eliminate malnutrition for the more than nine billion people by 2050. The findings from the pilots shed light on the potent
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Habyarimana, Ephrem, and Nicole Bartelds. "Yield Prediction in Sorghum (Sorghum bicolor (L.) Moench) and Cultivated Potato (Solanum tuberosum L.)." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_17.

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AbstractSorghum and potato pilots were conducted in this work to provide a solution to current limitations (dependability, cost) in crop monitoring in Europe. These limations include yield forecasting based mainly on field surveys, sampling, censuses, and the use of coarser spatial resolution satellites. We used the indexes decribing the fraction of absorbed photosynthetically active radiation as well as the leaf areas derived from Sentinel-2 satellites to predict yields and provide farmers with actionable advice in sorghum biomass and, in combination with WOFOST crop growth model, in cultivat
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Şen, Zekai. "Techniques to Predict Agricultural Droughts." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0010.

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In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, seve
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Awate, Pradnya, and Ajay D. Nagne. "Case Studies on Generative Adversarial Networks in Precision Farming." In Advances in Geospatial Technologies. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6900-5.ch011.

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The chapter reviews the applicability of Generative Adversarial Networks in precision agriculture, with an emphasis on its role in enhancing remote sensing technology. This ranges from resolution augmentation for satellite and drone images using GAN-based models like SRGAN and CycleGAN to generating synthetic data for training models that will help in crop health monitoring, soil analysis, and yield prediction. This case study demonstrates tremendous improvements in image quality and decision-making, with further reach into weather simulation, real-time UAV monitoring, and IoT integration.
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Gohil, Vishwa, Nitin Varshney, Alok Shrivastava, and Yogesh Garde. "RECENT DEVELOPMENT AND FUTURISTIC TRENDS OF DATA MINING IN AGRICULTURE." In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 5. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bact5p4ch3.

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Data mining is pivotal in revolutionizing agriculture by extracting valuable insights from vast and diverse datasets. Key data mining methods, including classification, clustering, and regression, are discussed in relation to critical tasks such as crop yield prediction, disease detection, and precision agriculture. This chapter delves into the applications of data mining techniques in agriculture, emphasizing their potential to enhance decision-making, optimize resource usage, and elevate overall crop productivity. Leveraging diverse data sources like satellite imagery, weather data, and sens
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Conference papers on the topic "Satellite crop prediction"

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Khan, Md Shadab, and Akanksha Singh. "Crop Yield Prediction via CNN-Transformer Architecture Leveraging Satellite Imagery and Weather Data." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985466.

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Bhavana, Kadaveru, Mamidi Rishika, Kanchana R, Deram Samhitha, Dosapati Deekshitha, and Kadari Vaishnavi. "Integrated Deep Learning Framework for Precision Crop Yield Prediction Using Satellite Imagery and Real-Time Environmental Data." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11063779.

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Kushwah, Satyam Singh, and Rajneesh Rani. "Skill Gradient Descent Algorithm Based Convolutional Neural Network for Plant Growth and Crop Yield Prediction using Satellite Images." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721656.

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Kashyap, Gautam Siddharth, Harsh Joshi, Manaswi Kulahara, et al. "Can AI See What We Can’t? Leveraging Deep Learning and Multi-Temporal Satellite Data to Revolutionize Crop Type Mapping and Yield Prediction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890344.

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Nwaogu, Chukwudi, Babatunde Alabi, Nasir A. Uma, Bridget E. Diag, Victor A. Agidi, and Chinwe G. Onwuagb. "LAND USE-COVER CHANGE TRAJECTORY AND IMPLICATION ON THE AGRICULTURAL AREAS OF SAO PAULO CITY: A GEOINFORMATICS APPROACH." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/2.1/s08.17.

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Agricultural productivity and environmental changes can be greatly affected by agricultural and other land use. Mapping of vegetation and land cover is a fundamental way of managing the natural resources on the earth surface. To determine or study the crop productivities of any geographical location, agricultural land use is one of the crucial clues for reliable information. We aimed to investigate the effects of urbanization on agricultural lands in Sao Paulo city. A 30-year multi-temporal satellite imagery dataset from four distinct years were mapped: 1992 (Landsat TM), 2002 (Landsat ETM+),
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Castro, R. Castro. "Prediction of yield and diseases in crops using vegetation indices through satellite image processing." In 2024 IEEE Technology and Engineering Management Society (TEMSCON LATAM). IEEE, 2024. http://dx.doi.org/10.1109/temsconlatam61834.2024.10717792.

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Simpson, George. "Crop yield prediction using a CMAC neural network." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196712.

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Rananavare, Laxmi B., and Sanjay Chitnis. "Crop Yield Prediction Using Satellite Imagery." In 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2023. http://dx.doi.org/10.1109/csitss60515.2023.10334198.

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Kavita and Pratistha Mathur. "Satellite-based Crop Yield Prediction using Machine Learning Algorithm." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544562.

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Kavita and Pratistha Mathur. "Satellite-based Crop Yield Prediction using Machine Learning Algorithm." In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2021. http://dx.doi.org/10.1109/asiancon51346.2021.9544562.

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