A Transform-Based Extraction and Advanced Cross-Attention-Aided Convolutional Stacked Sparse Autoencoder for Tomato Disease Detection.

Abstract

Low agricultural productivity is significantly influenced by plant diseases. Most farmers find it difficult to accurately recognize and effectively control plant diseases. Early disease detection is essential to minimizing future losses. This paper presents a novel deep learning framework for the precise categorization of tomato plant diseases. An Adaptive Threshold-Based Bilateral Filtering (ATBF) approach is used in the pre-processing step to improve image quality and reduce noise. Scale-Invariant Feature Transform (SIFT) is used for feature extraction, and the Coati Optimization Algorithm (COA), which lowers dimensionality by taking advantage of the coati's unique hunting and escape tactics, is used for feature selection. A Cross-Attention Aided Convolutional Stacked Sparse Autoencoder (CA-CSSAE) is proposed for classification, combining cross-attention mechanisms, sparse representation, and convolutional learning. The proposed model outperforms existing techniques in terms of precision, recall, and F-measure, achieving 99.5% accuracy across experiments on the PlantVillage dataset, which includes a variety of tomato diseases. PlantVillage dataset is a publicly available standard dataset. The proposed framework demonstrates effective tomato disease classification on the PlantVillage dataset and provides a robust approach for automated disease detection.