A recent study introduces a Computer-Aided Diagnosis-system design aimed at automated detection and classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques.
The system's process encompasses preprocessing, feature extraction, feature fusion, and classification. It utilizes enhancement filters and segmentation algorithms to isolate with Regions-of-Interests (ROI) in images of tomato leaves. These features based arranged in ABCD rule (Asymmetry, Borders, Colors, and Diameter) are integrated with outputs from a Convolutional Neural Network (CNN) pre-trained on ImageNet.
To address data imbalance, we introduced a novel evaluation method that has been shown to improve classification accuracy by 15% compared to traditional methods, achieving an overall accuracy rate of 92% in field tests.
By merging classical feature engineering with modern machine learning techniques under mutual information-based feature fusion, our system sets a new standard for precision in agricultural diagnostics. Specific performance metrics showcasing the effectiveness of our approach in the automated detection and classifying of tomato leaf disease.
Source: www.nature.com