Crop Prediction Models—A Review
Crop yield is directly dependent on climatic and weather conditions. A lot of research has been done studying the dependency of weather on crop yield. Crop prediction models have proven to be successful in increasing the crop yield. Soil parameters and atmospheric parameters are used by the models to predict the suitable crop. Parameters such as type of soil, pH, phosphate, potassium, organic carbon, sulphur, manganese, copper, iron, depth, temperature, rainfall, humidity have shown to influence the yield of crop. In this paper, we review the research conducted by several researchers in this direction with a logical conclusion.
KeywordsArtificial neural network (ANN) Weather Crop prediction models
The authors are grateful to Dr. Anitha C, Assistant Professor, Department of Computer Science & Engineering, The National Institute of Engineering, Mysuru for her guidance and helpful comments. The authors also would like to thank the HOD, Department of CSE and the Principal for their continuous support and encouragement.
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