CNN-BI-LSTM-CYP: A deep learning approach for sugarcane yield prediction

Abstract

Sugarcane (Saccharum officinarum L.) is one of the principal origins of sugar and is also known as the main cash crop of India. About 19.07% of the total production of the world’s sugar requirement is fulfilled by India. Traditionally, Statistical approaches have been utilized for Crop yield prediction, which is tedious and time-consuming. In this direction, the present work proposed a novel hybrid CNN-Bi-LSTM_CYP deep learning-based approach that includes convolutional layers to extract the relevant spatial information in a sequence to Bi-LSTM layers that recognize the Phenological long-term and short-term bidirectional dependencies in the dataset to predict the Sugarcane crop yield. The experimentation was performed and validated on the historical dataset from 1950 to 2019 years of the major Sugarcane-producing states of India. The preliminary results shown that the CNN-Bi-LSTM_CYP method performed well (RMSE:4.05, MSE:16.40) in comparison to traditional Stacked-LSTM (RMSE:8.8, MSE:77.79), ARIMA (RMSE:5.9, MSE:34.80), GPR (RMSE:10.1, MSE:103.3), and Holt-winter Time-series (RMSE:9.9, MSE:99.7) techniques. The study concluded that the predicted sugar yield has a minimal relative error concerning the ground truth data for the CNN-Bi-LSTM_CYP approach proving the proposed model's efficiency.