Machine Learning-Based Neural Network Models for Crop Identification, Weed Prediction, Yield Forecasting, and Cost Estimation in Agriculture

Abstract

This study employs a series of neural network-based machine learning models to address four core areas of crop production: A neural network model is trained on historical crop data, soil type, weather conditions, and regional specifics to suggest the most suitable crops for a given region. Weeds are predicted and classified based on satellite imagery and environmental data using convolutional neural networks (CNN). A recurrent neural network (RNN) model predicts crop yield based on weather patterns, soil health, and crop type, enhancing crop yield forecasting accuracy. A regression-based neural network is utilized to estimate the costs involved in crop production, accounting for inputs like labor, fertilizers, and water usage. The models were trained on a dataset comprising 10,000 records of crop and soil characteristics from multiple regions.