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

• Published in: Computers and electronics in agriculture 2024-12, Vol.227, p.109562, Article 109562
• Main Authors: Chang, N. Andy Kundang, Dey, Shouvik, Das, Dushmanta Kumar
• Format: Article
• Language: English
• Subjects: agriculture; Cache memory; crop yield; Crop yield prediction; drought; electronics; Feedforward neural network; memory; prediction; time series analysis; Time-dimension based problem; vegetation; yield forecasting
• ISSN: 0168-1699
• DOI: 10.1016/j.compag.2024.109562

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. •A novel concept known as Time-Dimension Based (TDB) problem is introduced to define the crop yield prediction problem.•TDB problem effectively describes the complex relationship between crop yield and the climatic conditions .•A novel neural network model, NSFFNet is proposed based on the concept of TDB problem.•NSFFNet is capable of capturing extreme climatic conditions that impact crop yield.•NSFFNet model outperforms time series models.