An ensemble deep learning approach for predicting cocoa yield

Abstract

One important aspect of agriculture is crop yield prediction. This aspect allows decision-makers and farmers to make adequate planning and policies. Before now, various statistical models have been used for crop yield prediction but this approach experienced some hiccups such as time wastage, inaccurate prediction, and difficulties in model usage. Recently, a new trend of deep learning and machine learning are now adopted for crop yield prediction. Deep learning can extract patterns from a large volume of the dataset, thus, they are suitable for prediction. The research work aims to propose an efficient deep-learning technique in the field of cocoa yield prediction. This research presents a deep learning approach for cocoa yield prediction using a Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) with Long Short Term Memory (LSTM). The ensemble approach was adopted because of the nature of the dataset used. Two different sets of the dataset were used, namely; the climatic dataset and the cocoa yield dataset. CNN-RNN with LSTM has some salient features, where CNN was used to handle the climatic dataset, and RNN was employed to handle the cocoa yield prediction in southwest Nigeria. Two major problems generated by the CNN-RNN model are vanishing and exploding gradients and this was handled by LSTM. The proposed model was benchmarked with other machine learning algorithms based on Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). CNN-RNN with LSTM gave the least mean of absolute error as compared to the other machine learning algorithms which shows the efficiency of the model.