Nested sequential feed-forward neural network: A cumulative model for crop yield prediction

This paper contends that framing crop yield prediction as a time-series problem imposes significant limitations. The varying climatic conditions, along with the distinct time frames associated with different stages of crop cultivation - such as sowing, vegetation growth, flowering, and harvest - present substantial challenges for accurately predicting crop yields. Additionally, the evolving climatic conditions over the years further complicate the prediction process. To address these challenges, this study introduces a novel perspective termed the 'Time-Dimension Based (TDB) Problem,' offering a conceptual framework that redefines how crop yield prediction should be approached. The TDB framework guides the modeling architecture into two layers: one for capturing the varying climatic conditions and the other for accumulating their impact on crops to determine the final yield. To implement this approach, the paper introduces the "Nested Sequential Feed-Forward Neural Network (NSFFNet)," a novel neural network architecture. NSFFNet features key components, including an innovative 'Nested Sequential Feed-Forwarding of Inputs' using feed-forward neural network for capturing Earth's climatic patterns over time, and a 'Neural Cache Layer' that utilizes cache memory to accumulate the cumulative impact of these patterns on crop yield. To validate this approach, a comprehensive evaluation of NSFFNet was conducted against traditional time-series models. The model was assessed for accuracy, generalizability, and robustness, particularly in estimating yields during drought years. NSFFNet consistently outperforms established models like RNN, 1D CNN, LSTM, GRU, and Transformer. These findings suggest that redefining crop yield prediction as a TDB problem is a highly effective strategy.