Enhancing Intercropping Yield Predictability Using Optimally Driven Feedback Neural Network and Loss Functions

• Published in: IEEE access Vol. 12; pp. 162769 - 162787
• Main Authors: Ikram, Amna, Aslam, Waqar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation models; Agricultural production; Agriculture; Agronomy; artificial neural network; Biological system modeling; Complexity; Crop yield; Decision making; Deep learning; Error analysis; Feedback; long short term memory; loss functions; Neural networks; Optimization; pea-cucumber intercropping; Prediction algorithms; Prediction models; Predictions; Predictive models; Propagation losses; Robustness; Smart agriculture; Soil measurement; Weight analysis; yield prediction
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3486101

Enhancing the crop yield predictability in intercropping systems is important for optimizing agricultural productivity. However, accurately predicting yield in such systems is quite challenging due to complex interactions between crops. This study introduces an advanced methodology using integrated loss functions within an optimally driven Feedback Neural Network (FNN) approach to improve yield prediction in a pea-cucumber intercropping systems. Traditional models relying only on Mean Square Error (MSE) loss function often unable to capture the complexity of models, leading to suboptimal performance. To address this limitation, the advanced loss functions are introduced like Dynamic Margin Loss (DML), Risk-Adjusted Loss (RAL), Quantile Loss (QL), and Hybrid Agronomic Efficiency Loss (HAEL) along with three optimizers such as Adaptive Momentum (Adam), Root Mean Square Propagation (RMSprop), and Adaptive delta (Adadelta). These loss functions incorporate risk, uncertainty, and agronomic efficiency into the model training process, enhances predictive capabilities and robustness. This proposed framework is able to capture the complexity of yield prediction by incorporating agricultural factors. While Gradient Boost Machines (GBM) and Long Short Term Memory (LSTM) have some potential, they are not able to capture these dynamics. The sensitivity and weight analysis also focuses that HAEL targets important agronomic factors such as nitrogen uptake and residue biomass, which provide a holistic view of yield prediction. The proposed approach improves the predictive performance compared to traditional models and helps to identify the importance of features, which makes it an effective tool for decision making in sustainable agriculture. Selecting appropriate loss functions is essential to improve the accuracy and robustness of crops yield prediction models. Thus, study provides a strong foundation for enhancing yield prediction in intricate intercropping systems, which all significantly enhance the advancement of precision agriculture.