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

TL;DR

A new deep learning model called PLDNet improves potato leaf disease classification by combining CNNs and Transformers with a novel adaptive activation function.

Contribution

The novel Adaptive Flatten p-Mish (AFpM) activation function enhances model performance with learnable adaptive nonlinearity.

Findings

PLDNet achieves 99.54% accuracy on the PlantVillage dataset and 87.50% on the Mendeley dataset.

AFpM improves classification accuracy by over 3% compared to Swish and Mish activation functions.

The model demonstrates strong generalization and scalability for automated plant disease detection.

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.

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