Predicting crop yields in Senegal using machine learning methods
Author:
Affiliation:
1. Laboratoire d'Océanographie, des Sciences de l'Environnement et du Climat (LOSEC), UFR Sciences et Technologies Université A. SECK de Ziguinchor Ziguinchor Senegal
2. IRD‐ESPACE‐DEV, Maison de la Télédétection Montpellier Cedex France
Publisher
Wiley
Subject
Atmospheric Science
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7947
Reference105 articles.
1. Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
2. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research
3. Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques
4. From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact
5. How can I deal with missing data in my study?
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1. Improving Rice Yield Prediction Accuracy Using Regression Models with Climate Data;Communications in Computer and Information Science;2024
2. An Extensive Study on Precision Farming Based on Crop Yield Using Integrated Approaches to Learning;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01
3. Modelling the impacts of water harvesting and climate change on rainfed maize yields in Senegal;Journal of Agricultural Engineering;2023-06-16
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