A hybrid CNN-transformer model with adaptive activation function for potato leaf disease classification.

Abstract

Potato plants are highly vulnerable to numerous diseases that can substantially affect both yield and quality. Conventional approaches for detecting these diseases are often labor-intensive, slow, and prone to inaccuracies, particularly under variable environmental conditions. This study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet), which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases. Furthermore, an adaptive parametric activation function, referred to as Adaptive Flatten p-Mish (AFpM), is proposed to enhance the model's learning flexibility and representational capacity. When evaluated on the PlantVillage and Mendeley datasets, PLDNet attains classification accuracies of 99.54% and 87.50%, respectively, surpassing contemporary state-of-the-art models and activation techniques. The proposed framework exhibits strong generalization performance and offers a scalable, efficient approach for automated plant disease identification. To highlight the novelty, the proposed AFpM activation function introduces a learnable parameter enabling adaptive nonlinearity, improving over Mish, Swish, and PFpM activation functions through dynamic gradient control. AFpM improves accuracy by 2.52% on Mendeley dataset, and 1.93% on PlantVillage dataset compared to PFpM, and by more than 3% compared to Swish and Mish.

Plant phenotyping relevance

葉画像から植物病害状態を推定する深層学習モデルと新規活性化関数を開発・評価しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper's phenotyping inputs are two public leaf-image datasets used directly for its classification experiments: the Mendeley Potato Leaf Disease dataset (explicitly deposited with URL) and the PlantVillage dataset via Kaggle (explicitly linked in Data availability). The authors' PLDNet code is only promised 'upon'