Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

• DOI: 10.32604/cmc.2022.027178
• Publication Date: 2022-04-21T07:04:55Z
• Authors: S. Vinson Joshua; A. Selwin Mich Pr...; Raju Kannadasan; Arfat Ahmad Khan; Worawat Lawanont; Faizan Ahmed Khan; Ateeq Ur Rehman; Muhammad Junaid Ali
• Keywords: Precision Agriculture

The exponential growth of population in developing countries like India should focus on innovative technologies the Agricultural process to meet future crisis. One vital tasks is crop yield prediction at its early stage; because it forms one most challenging precision agriculture as demands a deep understanding pattern with highly nonlinear parameters. Environmental parameters rainfall, temperature, humidity, and management practices fertilizers, pesticides, irrigation are very dynamic approach vary from field field. In proposed work, data were collected paddy fields 28 districts wide spectrum Tamilnadu over period 18 years. Statistical model Multi Linear Regression was used benchmark for prediction, which yielded an accuracy 82% owing ranging input data. Therefore, machine learning models developed obtain improved accuracy, namely Back Propagation Neural Network (BPNN), Support Vector Machine, General Networks given set. Results show that GRNN has greater 97% (R² = 0.97) normalized mean square error (NMSE) 0.03. Hence can be diversified geographical fields.