PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Abstract

Overlooked diseases in agriculture severely impact crop growth, which results in significant losses for farmers. Unfortunately, manual field visits for plant disease diagnosis (PDD) are costly and time consuming. Although various methods of PDD have been proposed, many challenges have yet to be investigated, such as early stage leaf disease diagnosis, class variations in diseases, cluttered backgrounds, and computational complexity of the diagnosis system. In this paper, we propose a Convolutional Neural Network (CNN)-based PDD framework (i.e., PDD-Net), which employs data augmentation techniques and incorporates multilevel and multiscale features to create a class and scale-invariant architecture. The Flatten-T Swish (FTS) activation function is utilized to prevent gradient vanishing and exploding problems, while the focal loss function is used to mitigate the impact of class imbalance during PDD-Net training. The PDD-Net method outperforms baseline models, achieving an average precision of 92.06%, average recall of 92.71%, average F1 score of 92.36%, and accuracy of 93.79% on the PlantVillage dataset. It also achieves an average precision of 86.41%, average recall of 85.77%, average F1 score of 86.02%, and accuracy of 86.98% on the cassava leaf disease dataset. These results demonstrate the efficiency and robustness of PDD-Net in plant disease diagnosis.

Plant phenotyping relevance

植物葉の病徴を画像から診断するCNN手法の開発とベンチマークが中心であり、植物の病害状態を直接推定するため、対象範囲に含める。

Code and data availability

The paper evaluates PDD-Net on two public benchmarks (PlantVillage and cassava leaf disease), but these are pre-existing community datasets, not paper-specific assets. No author code, trained models, or supplementary deposits with availability statements or URLs are mentioned in the supplied blocks.

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