Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management

Abstract

Plant disease detection is a critical task in agriculture, directly impacting crop yield, food security, and sustainable farming practices. This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn. The model leverages an advanced architecture comprising residual blocks for efficient feature extraction, attention mechanisms to enhance focus on disease-relevant regions, and lightweight layers for computational efficiency. These components collectively enable FourCropNet to achieve superior performance across varying datasets and class complexities, from single-crop datasets to combined datasets with 15 classes. The proposed model was evaluated on diverse datasets, demonstrating high accuracy, specificity, sensitivity, and F1 scores. Notably, FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset. Its scalability and ability to generalize across datasets underscore its robustness. Comparative analysis shows that FourCropNet consistently outperforms state-of-the-art models, such as MobileNet, VGG16, and EfficientNet, across various metrics. FourCropNet’s innovative design and consistent performance make it a reliable solution for real-time disease detection in agriculture. This model has the potential to assist farmers in timely disease diagnosis, reducing economic losses and promoting sustainable agricultural practices.

Plant phenotyping relevance

植物葉の病害状態を画像から推定するCNNモデルを開発し、複数作物データセットで性能評価しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper evaluates FourCropNet on a public Kaggle multi-class crop disease image dataset (reference [28]), which is the plant image input used for the paper's phenotyping/disease-detection measurements. No author code or trained model checkpoints are reported as publicly available.