An Innovative Design for Early Disease Detection in Apple Leaf and Fruit Using a Multi-Label Classification Model and Gaussian Bounding

Abstract

Early Disease Detection (EDD) in plants is crucial for identifying infections before serious symptoms manifest, ensuring agricultural product safety, reducing chemical use, improving orchard hygiene, and providing a sustainable disease control method. The research developed a EDALMG model to improve apple disease recognition and orchard productivity by combining RGB and multispectral imaging. It segments data from Kaggle datasets for training and creates vegetation indices to highlight plant physiology. The model uses a data-level fusion approach, channel attention, and hierarchical feature extraction through ResNet for comprehensive disease features. It employs multi-label classification to identify multiple diseases simultaneously, enhancing system robustness. The Gaussian bounding method is preferred for tracking infection areas due to its precise 2D positional data usage. The performance analysis includes Loss Calculation, Accuracy Calculation, Confusion Matrix Calculation, and F1-Score Calculation.

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