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Journal articles on the topic 'Keywords:- Humidity Prediction'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

A, Nischal Alpe, P. Kulkarni Aditya, V. Ganesh, and AV Karthik. "COMPARATIVE ANALYSIS OF DATA MINING ALGORITHMS FOR HUMIDITY PREDICTION." Journal of Research and Review: Fuzzy Logic Design and Fuzzy Systems 1, no. 1 (2025): 27–33. https://doi.org/10.5281/zenodo.15193131.

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<em>Predicting humidity is critical in weather forecasting, agriculture, and HVAC systems. This study looks into the many methods used for humidity prediction, which range from basic statistical models to cutting-edge machine learning techniques. We go over their benefits and downsides, as well as methods for enhancing forecast accuracy and data processing. Finally, the research looks at future trends, such as hybrid models, which mix different approaches to better forecasting.</em>

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Liu, Tianhong, Xianzhu Qiao, Sixing Liu, and Shengli Qi. "Research on Greenhouse Environment Prediction Based on GCAKF-CNN-LSTM." Applied Engineering in Agriculture 40, no. 2 (2024): 181–87. http://dx.doi.org/10.13031/aea.15867.

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Highlights A GCAKF-CNN-LSTM model is proposed for greenhouse temperature and humidity forecasting. The grey correlation analysis is used to select the most relevant variables. Kalman filter is applied for denoising to improve the data quality. The proposed model achieves higher forecasting accuracy with the lowest forecasting errors. Abstract. Accurate prediction of temperature and humidity in the greenhouse environment is helpful to regulate the environment and promote crop growth. Aiming at the characteristics of nonlinear and strong coupling in the greenhouse environment, this article propo

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GÖKDENİZ, KADİR, and ERKAN BOSTANCI. "Indoor Air Quality Predictions For Automation." Journal of Artificial Intelligence and Human Sciences 1, no. 1 (2024): 56–66. https://doi.org/10.5281/zenodo.14670650.

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<strong>Abstract: </strong>This study examines the implementation of home automation systems to predict indoor air quality using real-time data such as temperature, humidity, pressure, occupancy status, energy consumption, and window conditions. Due to the superior pattern recognition performance of recurrent neural networks, the study employs deep learning techniques for air quality prediction. A comparative analysis of GRU, LSTM and BiGRU models highlights GRU’s superior performance across various metrics, emphasizing its generalization capability. The study also introduces an Air-Smar

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Dhwani, N., P. Pranay, V. V. Haragopal, and T. Manjusha. "Year-wise Longitudinal Comparative Analysis of Temperature Prediction Models for Hyderabad (2017–2024)." Indian Journal Of Science And Technology 18, no. 27 (2025): 2179–84. https://doi.org/10.17485/ijst/v18i27.559.

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Objective: The study aims to compare and analyze temperature forecasting models for Hyderabad from 2017 to 2024 in the context of modeling long-term accuracy and the influence of critical meteorological conditions on temperature. Methods: Past meteorological data, including temperature, humidity, solar radiation, wind speed, and precipitation, were analyzed. Five models for predicting were applied: Multiple Linear Regression, Decision Tree Regression, Random Forest Regression, Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) Neural Networks. A chi-squared test determined the

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Zheng, Muzi, Brian Leib, Wesley Wright, and Paul Ayers. "Neural Models to Predict Temperature and Natural Ventilation in a High Tunnel." Transactions of the ASABE 62, no. 3 (2019): 761–69. http://dx.doi.org/10.13031/trans.12781.

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Abstract. As a response to the rising demand for local food, high tunnels (HTs) can help small producers become more profitable through crop protection and extension of the growing season. Proper ventilation that responds to changes in outside weather conditions can remove excess heat and humidity inside HTs and lead to better solar energy utilization while maintaining a favorable growth environment. Rather than depending on complex mathematical models, this study investigated an artificial neural network (ANN) for predicting the inside air temperature and ventilation rate of a HT. Energy bala

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Joshy, Ms S. Agnes, and S. Karthika. "Rainfall Prediction Using Machine Learning with Web Deployment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–7. https://doi.org/10.55041/ijsrem43869.

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Rainfall prediction plays a crucial role in agriculture, disaster management, and water resource planning. Traditional forecasting methods rely on statistical techniques, which may not effectively capture the complex patterns of weather data. This project utilizes machine learning (ML) techniques to enhance the accuracy of rainfall prediction. Various ML algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks are employed to analyse historical weather data, including temperature, humidity, wind speed, and atmospheric pressure. The proposed system preproce

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Kumar, Vikas, Vishal Kumar Yadav, and Er Sandeep Dubey. "Rainfall Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2494–97. http://dx.doi.org/10.22214/ijraset.2022.42876.

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Abstract: In India, Agriculture contributes major role to Indian economy. For agriculture, Rainfall is important but during these days’ rainfall prediction has become a major challenging problem. Good prediction of rainfall provides knowledge and know in advance to take precautions and have better strategy about theirs crops. Global warming is also having severe effect on nature as well as mankind and it accelerates the change in climatic conditions. Because of its air is getting warmer and level of ocean is rising, leads to flood and cultivated field is changing into drought. Due to adverse c

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Li, Yueh-Jung, Kuang-Wen Hsieh, Suming Chen, and Perng-Kwei Lei. "Establishing Modularized Environmental Measurements and Analyzing and Applying Environmental Information for a Windowless Broiler House in Taiwan." Transactions of the ASABE 62, no. 2 (2019): 303–13. http://dx.doi.org/10.13031/trans.12984.

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Abstract. The purpose of this study was to apply wireless sensor modules to measure the environmental conditions in a windowless broiler house. An environmental index monitoring system was established that automatically estimated the temperature-humidity index (THI) and temperature-humidity-velocity index (THVI) according to the collected database. Finally, prediction of the relationships between the THI or THVI and the feed conversion rate (FCR) was studied. Nine sets of temperature, relative humidity, and wind velocity sensors were used for measuring the inside environment. An additional set

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Mariano, Everson Batista, Enilson Palmeira Calvacanti, and Herika Pereira Rodrigues. "Avaliação do BRAMS na Previsão Numérica de Temperatura e Umidade Relativa do Ar para o Estado da Paraíba (Evaluation of BRAMS in Numerical Prediction of Temperature and Relative Humidity in State of Paraiba)." Revista Brasileira de Geografia Física 4, no. 3 (2011): 463. http://dx.doi.org/10.26848/rbgf.v4i3.232736.

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Neste trabalho foram avaliadas as previsões numéricas feitas pelo modelo numérico Brazilian Developments on the Regional Atmospheric Model System – BRAMS conforme execução em produção na UFCG. Foram analisadas as variáveis meteorológicas: temperatura do ar e umidade relativa do ar em superfície. A comparação foi feita com dados coletados em 19 estações meteorológicas automáticas, espalhadas pelos estados da Paraíba, Pernambuco e alagoas, pertencentes ao Instituto Nacional de Meteorologia - INMET. Os resultados mostram que os valores de temperatura do ar observados e estimados pelo modelo BRAMS

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Rhea Sriniwas, Dr. "Crop Harvesting using Machine Learning (ML) and Internet of Things (IOT)." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02803.

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Accurate crop prediction is essential for optimizing agricultural productivity and ensuring food security, particularly in regions where farming decisions are heavily influenced by soil and climatic conditions. Traditional crop recommendation systems often lack real-time adaptability and the ability to integrate diverse data sources such as soil sensors and live weather feeds. This project introduces a real-time crop prediction system that leverages a Support Vector Machine (SVM) classifier for precise and reliable crop recommendations. The system integrates heterogeneous data inputs, includin

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S, Mrs SARANYA. "Weather Forecast and it’s Visualization using Augmented Reality: Mobile App." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem29086.

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Weather forecasting is the process by which meteorologists project the weather for the future. These forecasts are dependent on a wide range of meteorological factors, including temperature, wind, humidity, rainfall, and a sizable dataset. Devices like the DHT11 sensor, which measures temperature and humidity and provides readings for a particular location, are used to collect data. The real-time integration of digital information into a user's surroundings is known as augmented reality, or AR. Augmented reality adds more details and improves the visual experience of the surrounding environmen

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Umami, Nurul, Ari Hernawan, and I. Gde Putu Wirarama Wedaswhara Wirawan. "PENERAPAN REGRESI LINEAR UNTUK PREDIKSI SUHU BADAN SAPI MENGGUNAKAN DATA SMART TERNAK DARI PT TELKOM INDONESIA: STUDI KASUS DI DESA PENGENGAT." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 6, no. 2 (2024): 448–60. http://dx.doi.org/10.29303/jtika.v6i2.376.

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Abstract West Nusa Tenggara (NTB) is one of the regions in Indonesia with significant potential in the field of livestock farming. In the context of the agricultural industry, livestock is considered a valuable asset, and monitoring their health is crucial for improving livestock productivity. This research aims to determine the relationship between the body temperature of cattle (Y) and environmental factors, namely air humidity (), environmental temperature (), and barometric pressure (), using smart livestock data provided by PT Telkom. The study was conducted in Pengengat Village, NTB, inv

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Jones, Petra, Richard Bibb, Melanie Davies, et al. "Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays." Journal of Diabetes Science and Technology 14, no. 1 (2019): 55–64. http://dx.doi.org/10.1177/1932296819877194.

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Background: Accurately predicting the risk of diabetic foot ulceration (DFU) could dramatically reduce the enormous burden of chronic wound management and amputation. Yet, the current prognostic models are unable to precisely predict DFU events. Typically, efforts have focused on individual factors like temperature, pressure, or shear rather than the overall foot microclimate. Methods: A systematic review was conducted by searching PubMed reports with no restrictions on start date covering the literature published until February 20, 2019 using relevant keywords, including temperature, pressure

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Rajapandiyan, Mr P. "Wildfire Risk Assessment System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04519.

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Abstract: This project aims to develop a predictive model for forest fire detection using historical fire datasets and weather report features. Leveraging Data Science and Machine Learning techniques, the model learns from past fire incidents—incorporating factors such as temperature, humidity, wind speed, and rainfall—to detect the likelihood of future fires. The system is built as a web application using Flask, offering real-time fire risk predictions through a simple user interface. The project pipeline includes data ingestion and cleaning, exploratory data analysis and visualization, featu

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Sumathi, S., and R. Rajesh. "Feature-Optimized Random Forest Model for Wildfire Prediction using Weather Information." Indian Journal Of Science And Technology 18, no. 19 (2025): 1530–37. https://doi.org/10.17485/ijst/v18i19.442.

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Objectives: Forest fires present a notable danger to economies and human communities. Accurate forest fire forecasting can support timely reactions, resource allocation, and effective management strategies. Methods: The Recursive Feature Elimination with Cross-Validation approach is used to extract the key features from the dataset that was obtained from Kaggle. This technique combines feature selection and cross-validation to ensure the selected features are well-suited to new data. The Random Forest regressor, which constructs several decision trees and combines their predictions to provide

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Surve, Rupali B., Ujwal A. Lanjewar, and Liladhar R. Rewatkar. "Deep Learning and NWP: An Evaluation for Accurate Weather Forecasting." Indian Journal Of Science And Technology 18, no. 28 (2025): 2273–82. https://doi.org/10.17485/ijst/v18i28.861.

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Objectives: To compare time series prediction-based deep learning techniques with Numerical Weather Prediction (NWP) to identify the optimal model for weather forecasting based on accuracy and consistency. Methods: The study utilizes a weather dataset collected from Kaggle, containing meteorological parameters such as temperature, humidity, and wind speed. Deep learning models such as the Radial Basis Function Network, the Convolutional Neural Network, the Recurrent Neural Network, and the Long Short-Term Memory have been developed and trained using the dataset for time-series forecasting. The

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Khatraty, Yahjeb Bouha, Nédra Mellouli Nauwynck, Mamadou Tourad Diallo, and Mohamedade Farouk Nanne. "Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture." International Journal of Emerging Technology and Advanced Engineering 12, no. 11 (2022): 79–88. http://dx.doi.org/10.46338/ijetae1122_09.

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Managing time series data generated by the intelligent objects around us requires cleaning and processing techniques, as well as a prediction model with high accuracy and low complexity. This study attempts to fuse climate time series data (Temperature, Humidity, Wind speed and Rainfall) and remote sensing data to predict rice yield. We performed statistical analysis of the data, additive decomposition, and measured the correlation between the different variables. We checked the stationarity of the data by using the Augmented Dickey-Fuller ADF test to apply statistical prediction models based

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Xin, Pingping, Haihui Zhang, Jin Hu, Zhiyong Wang, and Zhen Zhang. "An Improved Photosynthesis Prediction Model Based on Artificial Neural Networks Intended for Cucumber Growth Control." Applied Engineering in Agriculture 34, no. 5 (2018): 769–87. http://dx.doi.org/10.13031/aea.12634.

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Abstract. The existing photosynthetic rate prediction models consider only a single growing season. However, a photosynthetic rate prediction model intended for full growth of crops is needed. Therefore, a photosynthetic rate prediction model based on artificial neural networks (ANN), which establishes the prediction of the entire photosynthetic process, is presented in this article. The proposed model was developed using the multi-factor photosynthetic rate data obtained by experiments on cucumber seedlings and flowering stage. The ANN model was trained with the Levenberg-Marquardt (LM) train

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KT, Tejashwini. "Cloud Burst Prediction System”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47769.

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Abstract- Cloudbursts are an extremely short duration, high intensity rainfall events that straits to flash floods, landslides leading to loss of life and property particularly in mountainous and vulnerable regions. It is already difficult to predict such phenomena because of their spatially localized or short-lived nature. The present study introduces a Cloudburst Prediction System that combines instantaneous weather data, satellite-based observations to predict and provide early warning signals for an upcoming cloudburst. The system uses atmospheric pressure, humidity, temperature, wind flow

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S., Sumathi, and Rajesh R. "Feature-Optimized Random Forest Model for Wildfire Prediction using Weather Information." Indian Journal of Science and Technology 18, no. 19 (2025): 1530–37. https://doi.org/10.17485/IJST/v18i19.442.

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Abstract <strong>Objectives:</strong> Forest fires present a notable danger to economies and human communities. Accurate forest fire forecasting can support timely reactions, resource allocation, and effective management strategies. <strong>Methods:</strong> The Recursive Feature Elimination with Cross-Validation approach is used to extract the key features from the dataset that was obtained from Kaggle. This technique combines feature selection and cross-validation to ensure the selected features are well-suited to new data. The Random Forest regressor, which constructs several

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Prabhu, Ms Ruta. "AI in Climate Science: Leveraging XGBoost and Random Forest for Ocean Temperature Forecasting." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49816.

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Abstract: By examining meteorological and oceanographic variables such as wind patterns, humidity, and air temperature, this study investigates the application of machine learning approaches, Random Forest Regressor and XGBoost Regressor, to forecast sea surface temperature (SST). Using an El Niño dataset, the study applies rigorous preprocessing to integrate spatial-temporal variability and address data discrepancies. Comparative research indicates that XGBoost outperforms Random Forest in terms of prediction accuracy, as seen by higher R2 scores and lower RMSE. The findings provide valuable

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Sanhaji, Ganis, Hamdi Sholahudin, and Imam Arief Rahman. "Internet of Things (IoT) Based Micro Climate Control Optimization System for Tropical Greenhouses in Responding to Climate Change." International Journal of Research and Review 11, no. 9 (2024): 141–51. http://dx.doi.org/10.52403/ijrr.20240916.

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This research aims to develop an Internet of Things (IoT) based cooling system that is integrated with Blynk, using a fan cooling system to monitor and regulate temperature, humidity and light intensity in the room. This system was evaluated for eight days by comparing sensor data with conventional measuring instruments. The results show a low coefficient of determination (R^2), indicating high accuracy in data prediction. The study found that these IoT systems effectively provide real-time data, enabling rapid response to environmental changes with an overall average error rate of less than 5

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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 (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 e

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Pedder, M. A., M. Haile, and A. J. Thorpe. "Short period forecasting of catchment-scale precipitation. Part I: the role of Numerical Weather Prediction." Hydrology and Earth System Sciences 4, no. 4 (2000): 627–33. http://dx.doi.org/10.5194/hess-4-627-2000.

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Abstract. A deterministic forecast of surface precipitation involves solving a time-dependent moisture balance equation satisfying conservation of total water substance. A realistic solution needs to take into account feedback between atmospheric dynamics and the diabatic sources of heat energy associated with phase changes, as well as complex microphysical processes controlling the conversion between cloud water (or ice) and precipitation. Such processes are taken into account either explicitly or via physical parameterisation schemes in many operational numerical weather prediction models; t

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Parab, Ms Sayali. "IoT Based Smart Agriculture Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35175.

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Agriculture is one of the crucial sources of economic growth in the country. Agriculture plays a major role in increasing the overall economy of any country. Many countries are having tremendous growth in the production of crops as the demand for food grains and supply increases. India is one of the leading countries in producing a variety of different crops. However, most parts of India are still using the traditional methods for implementation and cultivation of crops, due to which farmers are facing a loss in their production due to inadequate supply of fertilizers and uncertain climatic co

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RUPADEVI, RUPADEVI. "Machine Learning for Sustainable Forecasting: Adaptive Wind Speed Prediction Using Functional Data." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02949.

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The global switch to sustainable and clean electricity sources depends heavily on wind energy. To ensure grid stability, minimise operating costs, and optimise the efficiency of wind energy systems, accurate wind speed forecasts is crucial. Using functional data from past weather patterns, this study proposes an adaptive machine learning-based method for wind speed prediction. Time-based indicators, temperature, humidity, atmospheric pressure, dew point, and other important meteorological characteristics are included in the dataset, which was gathered via the Open-Meteo weather API for the yea

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Jojo, Joan Mary, Jins Jushu, Suryakiran S, and Surya R. "Impact of Cyclic Moisture on Shrinkage of Glue-laminated Softwood." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–8. https://doi.org/10.55041/ijsrem.icites012.

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Abstract. Glulam softwood, widely used in construc- tion due to its strength-to-weight ratio and sustainabil- ity, undergoes dimensional changes under varying moisture conditions. Cyclic moisture exposure acceler- ates these changes, potentially affecting its long-term performance. This study investigates the impact of cy- clic moisture exposure on the shrinkage behaviour of glue-laminated (glulam) softwood, which affects struc- tural integrity and dimensional stability in engineered wood products. The experimental methodology in- volves fabricating glulam specimens using industry- standard ad

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Jiménez, Andrés F., Brenda V. Ortiz, Luca Bondesan, Guilherme Morata, and Damianos Damianidis. "Evaluation of Two Recurrent Neural Network Methods for Prediction of Irrigation Rate and Timing." Transactions of the ASABE 63, no. 5 (2020): 1327–48. http://dx.doi.org/10.13031/trans.13765.

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HighlightsNARX and LSTM recurrent neural networks were evaluated for prediction of irrigation prescriptions.LSTM neural networks presented the best performance for irrigation scheduling using soil matric potential sensors.NARX neural networks had the best performance for predicting irrigation prescriptions using weather data.High performance for several time-ahead predictions using both recurrent neural networks, with R2 &gt; 0.94.The results can be adopted as a decision-support tool in irrigation scheduling for fields with different types of soils.Abstract. The implementation of adequate

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Parhwal, Divyam. "DATA ANALYSIS OF METROLOGICAL DATA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34399.

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This project report is set to give an interactive visualization and analytical presentation for Meteorological records in Finland. These Meteorological records Data of Finland is recorded by way of integrating the three current infrastructures for numerical weather prediction, observational information and satellite tv for pc image processing and this is recorded. The Meteorological data used in the study consists of near- floor atmospheric elements including wind direction, apparent temperature, cloud layer(s), ceiling peak, visibility, current weather, wind velocity, cloud cowl and precipita

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Huang, Hui, Shuchang Liu, Junaid Ullah, et al. "Model Maintenance of RC-PLSR for Moisture Content Measurement of Dried Scallop." Transactions of the ASABE 63, no. 4 (2020): 891–99. http://dx.doi.org/10.13031/trans.13728.

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HighlightsThe RC-PLSR model for Haiwan scallop can be transferred to Xiayi scallop.The direct standardization method is suggested for model maintenance.The VSWS-PDS method can be further improved in precision.Abstract. A prediction model for evaluating the moisture content in dried Haiwan scallops was established using hyperspectral imaging (HSI) technology in a previously published study. The accuracy of such models is usually affected by differences in sample species, different environmental conditions such as temperature or humidity, and aging of instruments. In this study, the prediction a

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Inam, Ali Supro, Ahmed Mahar Javed, and Ali Mahar Shahid. "Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness." Indian Journal of Science and Technology 13, no. 13 (2020): 1367–79. https://doi.org/10.17485/IJST/v13i13.79.

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Abstract <strong>Objectives:</strong> This study aims to implement data analytics and machine learning approaches to rice data and to establish association rules on fixed attributes and their correlations for yield prediction of crops. <strong>Methods:</strong> The data of rice crop is collected from district Larkana as per defined parameters: area, production, yield, temperature, rainfall, humidity and wind speed. The pre-processing operations are applied on prepared dataset to execute data analytics and machine learning algorithms. The processed data are then input into an Apr

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Kadu,, Shrutika. "Weather Forecasting System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41776.

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The IoT-Based Weather Forecasting System is an advanced embedded solution designed for smart agriculture and environmental monitoring. This system leverages IoT and embedded technology to provide real-time data collection and remote-control accessibility across India. It integrates various sensors to monitor real-time temperature, humidity, and soil moisture, ensuring continuous environmental tracking. The collected data is transmitted to a mobile application and a cloud server for seamless monitoring. A key feature of this system is its wild animal detection mechanism, which sends instant ale

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Singh, Dr Ajay Kumar. "A Hybrid Approach to Crop Disease Prediction: Combining Environmental and Image Data for Enhanced Accuracy." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47080.

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Abstract - This study introduces a comprehensive methodology for predicting crop diseases by integrating Machine Learning (ML) techniques with Deep Learning (DL) models, aimed at aiding farmers in the early detection of plant diseases and optimizing crop selection. The proposed system utilizes a Random Forest Classifier for crop recommendations, taking into account essential agricultural factors such as nitrogen, phosphorus, potassium (NPK) levels, soil pH, temperature, humidity, and rainfall. Concurrently, MobileNetV2-based Convolutional Neural Networks (CNNs) are utilized for the identificat

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A, Sehkammal. "Analysing the Accuracy of Crop Yield Prediction Using Deep Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 201–27. http://dx.doi.org/10.22214/ijraset.2021.37911.

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Abstract: The Indian farming level decreases step by step inferable from certain components like inordinate usage of pesticides, water level decrement, environment changes, and unpredicted precipitation, and so forth on the farming information, elucidating investigation is performed to comprehend the creation level. The creation of yields isn't expanded inferable from these issues that influences the economy of farming. By utilizing AI strategies, the harvest from given dataset need to foresee by farming areas for forestalling this issue. Yield forecast is of extraordinary importance for yield

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Sumarminingsih, Eni, Suci Astutik, Nur Silviyah Rahmi, Aqsa Yudhistira Redi, and Natasha Aulia. "Rainfall Modeling using a Machine Learning Approach as Support for the Early Warning System for Flood Disasters in Malang Regency, Indonesia." INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 20, no. 02 (2024): 373. https://doi.org/10.59467/ijass.2024.20.373.

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Malang is one of the areas in East Java - Indonesia that has the potential to experience flooding caused by high rainfall. Floods have a negative impact on both agriculture and public health. Therefore, it is necessary to create a rainfall prediction model in Malang that supports the creation of an early warning system for flood disasters. In this research, a machine learning approach was used, namely the Long Short Term Memory model, to model monthly rainfall at three stations in Malang Regency, namely Abd. Saleh Airbase Station, Karangkates Station and Karangploso Station. Several inputs wer

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Rahman, Anisur, Mohammad Akbar Faqeerzada, Rahul Joshi, et al. "Quality Analysis of Stored Bell Peppers Using Near-Infrared Hyperspectral Imaging." Transactions of the ASABE 61, no. 4 (2018): 1199–207. http://dx.doi.org/10.13031/trans.12482.

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Abstract. The objective of this study was to predict the moisture content (MC), soluble solids content (SSC), and titratable acidity (TA) content in bell peppers during storage (18°C, 85% relative humidity) over 12 days, based on near-infrared hyperspectral imaging (NIR-HSI) in the 1000-1500 nm wavelength range. The mean spectra of 148 mature bell peppers were extracted from the hyperspectral images, and multivariate calibration models were built using partial least squares (PLS) regression with different preprocessing spectra techniques. The most effective wavelengths were selected using the

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Mamenun, Mamenun, Yonny Koesmaryono, Rini Hidayati, Ardhasena Sopaheluwakan, and Bambang Dwi Dasanto. "Kemajuan Penelitian Pemodelan Prediksi Demam Berdarah Dengue menggunakan Faktor Iklim di Indonesia : A Systematic Literature Review." Buletin Penelitian Kesehatan 49, no. 4 (2021): 231–46. http://dx.doi.org/10.22435/bpk.v49i4.4762.

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Since discovered firstly in 1968, number of cases and areas affected by DHF in Indonesia has been increased. In 2019, dengue cases have found in all provinces within 481 districts/cities (94%). Our research is conducted to analyze the current status and gaps of climate relationship and its modeling to DHF in Indonesia. A systematic searching of literature was carried out through the search engine PubMed and Google Scholar. The method includes determining questions, publication period, keywords, and criteria of literature. Thirty-two literatures have been selected according to the criteria. The

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Urquizo, Jessica, Mirtha Jiménez, Pedro Aguilar, and Wilson Chango. "Fighting moniliasis in Orellana with sensors and PWA for sustainable agriculture." Bionatura Journal 1, no. 1 (2024): 1–14. http://dx.doi.org/10.70099/bj/2024.01.01.6.

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The primary objective of this research was to enhance cocoa production and quality in tropical countries, such as Latin America and Africa, where cocoa cultivation plays a pivotal role in the economy of rural communities. The primary challenge addressed in this study was moniliasis, a fungal disease that affects cocoa fruits and leads to a significant decline in crop production and quality. A multidisciplinary approach was employed to tackle this issue, combining sensors, MongoDB Compass databases, Progressive Web Applications (PWAs), and predictive models. A research methodology incorporating

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Urquizo, Jessica, Mirtha Jiménez, Pedro Aguilar, and Wilson Chango. "Fighting moniliasis in Orellana with sensors and PWA for sustainable agriculture." Bionatura Journal 1 1, no. 1 (2024): 1–15. http://dx.doi.org/10.21931/bj/2024.01.01.5.

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The primary objective of this research was to enhance cocoa production and quality in tropical countries, such as Latin America and Africa, where cocoa cultivation plays a pivotal role in the economy of rural communities. The primary challenge addressed in this study was moniliasis, a fungal disease that affects cocoa fruits and leads to a significant decline in crop production and quality. A multidisciplinary approach was employed to tackle this issue, combining sensors, MongoDB Compass databases, Progressive Web Applications (PWAs), and predictive models. A research methodology incorporating

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Urquizo, Jessica, Mirtha Jiménez, Pedro Aguilar, and Wilson Chango. "Fighting moniliasis in Orellana with sensors and PWA for sustainable agriculture." Bionatura 9, no. 1 (2024): 1–16. http://dx.doi.org/10.21931/rb/2024.09.01.5.

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The primary objective of this research was to enhance cocoa production and quality in tropical countries, such as Latin America and Africa, where cocoa cultivation plays a pivotal role in the economy of rural communities. The primary challenge addressed in this study was moniliasis, a fungal disease that affects cocoa fruits and leads to a significant decline in crop production and quality. A multidisciplinary approach was employed to tackle this issue, combining sensors, MongoDB Compass databases, Progressive Web Applications (PWAs), and predictive models. A research methodology incorporating

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Kurniawan, Hary, Nursigit Bintoro, and Joko Nugroho W.K. "PENDUGAAN UMUR SIMPAN GULA SEMUT DALAM KEMASAN DENGAN PENDEKATAN ARRHENIUS (Shelf Life Prediction of Palm Sugar on Packaging using Arrhenius Equation)." Jurnal Ilmiah Rekayasa Pertanian dan Biosistem 6, no. 1 (2018): 93–99. http://dx.doi.org/10.29303/jrpb.v6i1.68.

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The purpose of this research was to determined the shelf life of packaged palm sugar at various temperatures and relative humidity (RH) storage using Arrhenius model based on changes in water content. The palm sugars were packed with 0.675 mm polyethylene packaging and stored at 15, 25, 30 and 35°C at RH of 77% and 98%. Measured parameters included the determination of critical parameters of palm sugar, initial moisture content and critical moisture content of palm sugar, changes in moisture content during storage. The Arrhenius model approach was used in this study to predict the shelf life p

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Setyanngsih, Fatma Agus. "IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 4, no. 2 (2017): 198. http://dx.doi.org/10.20527/klik.v4i2.105.

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<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 12

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Bhakti, S. Pimpale, and A. Pandit Anala. "Multioutput Ensemble Machine Learning Algorithm: A Prediction Model of Acute Respiratory infection and Pneumonia Occurrence." Indian Journal of Science and Technology 16, no. 45 (2023): 4141–55. https://doi.org/10.17485/IJST/v16i45.1011.

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Abstract <strong>Objectives:</strong> To forecast daily OPD patients based on air pollution and weather parameters, the objective is to build a robust model that accurately predicts patient volume by considering major missing values and factors such as PM2.5 levels, temperature, humidity, wind speed, and rainfall, etc. thereby improving healthcare planning and delivery. <strong>Methods:</strong> To develop the multioutput ensemble model for forecasting daily OPD (out-patient department), we have used 13 machine learning techniques such as regression analysis, Extra tree regresso

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Armstrong, Paul R., Elizabeth B. Maghirang, Subramanyam Bhadriraju, and Samuel G. McNeill. "Equilibrium Moisture Content of Kabuli Chickpea, Black Sesame, and White Sesame Seeds." Applied Engineering in Agriculture 33, no. 5 (2017): 737–42. http://dx.doi.org/10.13031/aea.12460.

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Abstract. Sesame and chickpea are important crops in Ethiopia because both are major export crops that generate much revenue for both small farmers and the country as a whole. However, there is a lack of information about the fundamental equilibrium moisture content (EMC) relationships among these crops, which would help facilitate better monitoring and storage. Therefore, EMC adsorption and desorption prediction models based on temperature (T) and relative humidity (RH) were developed for the modified Chung-Pfost and modified Henderson models for Kabuli chickpea (KC), black sesame (BS), and w

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Johny, Juliet, and Linda Sara Mathew. "A Framework for Forecasting Outbreak of Infectious Diseases Based on Climate Variability and Social Media Content." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 118–24. http://dx.doi.org/10.35940/ijrte.e5204.019521.

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The amount of data has risen significantly over the last few years, due to the popularity of some of the data generation sources like social media, electronic health records, sensors and online shopping sites. Analyzing, processing and storing this data is very prominent since it helps to uncover hidden patterns and unknown correlations. A big data analysis and prediction System is proposed in this context, which combines weather observations, health data and social media content in order to forecast the outbreaks of infectious diseases in a locality. Finding information about the determinants

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Shekar, Padala Raja. "Rainfall-Runoff Modelling of a River Basin Using HEC HMS: A Review Study." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 506–8. http://dx.doi.org/10.22214/ijraset.2021.38004.

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Abstract: A hydrological model helps in understanding of the hydrological processes and useful to measure water resources for effective water resources management. Hydrological cycle describes evaporation, condensation, precipitation and collection of earth water and on again. Hydrological models have been used in different watersheds across the world. The runoff estimation process is the most complex in nature that depends on the meteorological data and also on the various watershed physical parameters. To generate runoff data for a particular watershed it is needed to find out various parame

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Prakash, Bhagwati, and Terry J. Siebenmorgen. "Single-Parameter Thin-Layer Drying Equations for Long-Grain Rice." Transactions of the ASABE 61, no. 2 (2018): 733–42. http://dx.doi.org/10.13031/trans.12555.

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Abstract. The use of multiple parameters in thin-layer drying equations makes it difficult to compare and quantify the impact of drying air temperature, relative humidity, and other factors on the drying characteristics of an agricultural crop. In this study, two single-parameter equations are proposed to quantify thin-layer drying characteristics of contemporary long-grain rice cultivars grown in the Mid-South U.S. Drying runs were first performed to obtain drying curves for cultivar ‘Roy J’ under 18 air conditions; several drying equations were evaluated for their fit to each drying curve. T

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Lewis, Micah A., Samir Trabelsi, and Stuart O. Nelson. "Estimating Energy Costs of Nonbeneficial Dryer Operation by Using a Peanut Drying Monitoring System." Applied Engineering in Agriculture 34, no. 3 (2018): 491–96. http://dx.doi.org/10.13031/aea.12624.

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Abstract. Knowledge of kernel moisture content during peanut drying is important to ensure that the bed of peanuts is dried appropriately. However, the lack of a commercially available, industry-accepted solution for real-time kernel moisture content determination during peanut drying makes its detection cumbersome and laborious. Samples of unshelled peanuts are extracted from the semitrailer by an operator periodically, and the samples have to be cleaned and shelled to determine kernel moisture content with the official meter. A peanut drying monitoring system that includes a microwave kernel

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Xiong, Yijie, Guoming Li, Naomi C. Willard, Michael Ellis, and Richard S. Gates. "Modeling Neonatal Piglet Rectal Temperature with Thermography and Machine Learning." Journal of the ASABE 66, no. 2 (2023): 193–204. http://dx.doi.org/10.13031/ja.14998.

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Highlights The rectal temperature and maximum ear base temperature were measured for neonatal piglets after birth. Piglets’ rectal temperature dropped on average 5.1 °C and reached 33.6 °C 30-min after birth. Machine learning algorithms were evaluated to predict piglet rectal temperature using ear temperatures. Machine learning model performance was compared to that of a direct regression using maximum ear base temperature. The best machine learning model was 0.2°C more accurate than the direct linear regression model. Abstract. Piglet body temperature can drop rapidly after birth, and the mag

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Juliet, Johny, and Sara Mathew Linda. "A Framework for Forecasting Outbreak of Infectious Diseases Based on Climate Variability and Social Media Content." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 5 (2021): 118–24. https://doi.org/10.35940/ijrte.E5204.019521.

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<strong>Abstract:</strong> The amount of data has risen significantly over the last few years, due to the popularity of some of the data generation sources like social media, electronic health records, sensors and online shopping sites. Analyzing, processing and storing this data is very prominent since it helps to uncover hidden patterns and unknown correlations. A big data analysis and prediction System is proposed in this context, which combines weather observations, health data and social media content in order to forecast the outbreaks of infectious diseases in a locality. Finding informa

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