Crop Yield Prediction of Indian Districts Using Deep Learning

The uncertain yield of crops is one of the major problems the agricultural sector faces today, especially in India. The objective of this paper is to provide an accurate and reliable prediction of crop yield. This will help farmers make decisions that can make their farming more efficient and profitable. We propose a novel deep learning model - an ensemble neural network model using Long Short-Term Memory (LSTMs) and one-dimensional Convolutional Neural Networks (CNNs). We used crop data for over 30 crops from 1997-2015 of all Indian districts. Our model substantially outperforms all other models (Linear Regression, Random Forest, extreme Gradient Boosting (XGB) Regressor, Feed-forward Neural Network (FFNN)) that were tested on accuracy in predicting crop yields. We achieve a correlation coefficient value of over 0.90 and 0.92 for our model for train and test datasets.Our model has several advantages compared to other models. Firstly, it is able to capture the time dependency on temperature and rainfall. Secondly, it is able to work on a large and diverse dataset, unlike most models which only perform well in small regions. Lastly, it is able to use several diverse features - geographical, social, and economic to make a prediction.