OPTIMIZING CROP YIELD PREDICTION CROP YIELD PREDICTION : A HYBRID APPROACH INTEGRATING CNN AND LSTM NETWORKS - Wang, Hui Hui; Wang, Yin Chai; Wee, Bui Lin; Jane Yan, Khoo; Farashazillah, Yahya (Little Lion Scientific, 2024)
Artificial Intelligence (AI) has proven successful in revolutionizing the agricultural sector, facilitating advancements in prediction, decision-making, and the monitoring and analysis of crops and soil. In this study, a hybrid model is introduced with the capability to predict crop yield. The proposed learning model combines the strengths of Convolutional Neural Network (CNN) with Recurrent Neural Network (RNN) models. CNN, recognized for its superior performance in feature extraction, is selected for its characteristic of considering a smaller number of parameters in the network, thereby reducing the risk of overfitting. Simultaneously, RNN serves as the prediction model, capitalizing on its inherent learning nature, feedback network, and ability to encode temporal sequence information. Addressing the short-term memory behaviour of RNN, the network is enhanced with LSTM cells, enabling effective long-term memory tasks. LSTM introduces memory blocks to resolve the exploding and vanishing gradient problem, differentiating itself from conventional RNN units. The best environment parameters have been identified by using the correlation where it shows the parameter that have the most significant relation with the crop production. The A Hybrid Approach Integrating CNN and LSTM Networks has achieved 74% accuracy in crop yield prediction.