Crop Yield Prediction Using Deep Learning

Agriculture provides a living for around 58% of India’s population. Agriculture, forestry, and fisheries were expected to generate ₹19.48 lakh crore in FY20. Given the significance of agriculture in India, farmers might benefit from early forecasting of agricultural yields. The study focuses on predicting agricultural yield, for Karnataka state using the regression with neural network model. The final constructed dataset takes parameters like agricultural area, crop, taluka, year, season, district wise annual rainfall (mm), district wise maximum and minimum temperature (°C) and harvest or yield for the time period of 1997–2017. The underlying model is built utilizing a Multilayer Perceptron Neural Network, a ReLu Activation function, an Adam Optimizer, and 50 epochs with a batch size of 200. The end of the training gained 96.43% accuracy on test data. Several additional well-known regression algorithms such as Multinomial Linear Regression, Random Forest Regression, and Support Vector Machine are also constructed and trained using the same dataset so as to compare their performance to the base model. From the final comparison results it was found that neural network model has outperformed classic machine models for crop yield prediction in terms of both mean absolute error and accuracy.