# Advancing plant disease classification using an attention-based CNN for intra-dataset and cross- dataset training

## TL;DR

This paper introduces an attention-based CNN that improves plant disease classification accuracy both within and across datasets, supporting better agricultural productivity and food security.

## Contribution

A novel attention-based CNN that effectively combines intra-dataset and cross-dataset training for plant disease classification.

## Key findings

- The model achieved 99.38% accuracy on potato leaf disease classification within the PlantVillage dataset.
- It reached 82.93% average accuracy for corn leaf diseases in cross-dataset training using the CD&S dataset.
- The model outperformed existing techniques in both intra- and cross-dataset scenarios.

## Abstract

The precise classification of plant diseases is crucial for ensuring food security for all people and boosting agricultural productivity. Although there has been significant progress in this field using deep learning approaches, cross-dataset training hasn’t drawn as much attention from researchers as intra-dataset training has. Moreover, very few models have successfully blended intra-dataset and cross-dataset training approaches. This paper proposes a novel attention-based Convolutional Neural Network (CNN) to overcome these limitations. The model improves feature extraction and classification accuracy across multiple datasets by using attention mechanisms. It was tested on five datasets (Digipathos, Northern Leaf Blight (NLB), PlantVillage, PlantDoc, and the CD&S dataset) that covered leaf diseases of both corn and potatoes. During intra-dataset training, the model achieved the highest classification accuracy of 99.38% when trained on images of potato leaves from the PlantVillage dataset. During cross-dataset training, the model exhibited the highest average classification accuracy of 82.93% for corn leaf diseases when trained on images from the CD&S dataset with their backgrounds removed. When compared to the techniques taken into consideration in this study under comparable experimental conditions, the results demonstrate improved performance. This study shows how the model may be flexible for both intra- and cross-datasets, offering a flexible way to categorize diseases that affect plants. Because of its ability to generalize across different datasets, it may be helpful in real-world agricultural applications with a wide variety of image quality and situations. This encourages the advancement of precision farming techniques and disease control.