AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases - Oluwatoyin Joy Omole; Renata Lopes Rosa; Muhammad Saadi; Demóstenes Zegarra Rodriguez (AI)
Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks.