Design and Development of a Deep Learning-Based System for Multi-Fruit Disease Classification and Severity Detection Using VGG-16 and VGG-19 Architectures on an Expert-Verified Indian Fruit Crop Dataset

Abstract

Fruit diseases caused by fungal, bacterial, and viral pathogens cause devastating pre- and post-harvest losses to Indian agriculture, particularly in Maharashtra where Guava, Mango, Orange, Papaya, and Pomegranate are major horticulture crops. Accurate, early-stage disease identification directly impacts farmer income, food security, and precision crop management. Conventional manual inspection by agronomists is subjective, time-consuming, and not scalable across thousands of orchard acres. Automated deep learning-based image analysis has emerged as a transformative solution, offering high accuracy, speed, and field deployability. Existing deep learning models for plant disease detection are predominantly trained on the real time database, which inadequately represents Indian fruit crop species. Furthermore, published systems focus on binary disease presence detection and lack disease severity grading — a critical requirement for treatment decision-making. The absence of expert-verified datasets and the visual overlap among disease classes (such as Phytophthora vs. Scab in Guava, and Powdery Mildew vs. Ring-spot in Papaya) present significant classification challenges. Limited training data for rare classes such as Stylerandroot (Guava, 310 samples) and Bacterial Blight (Pomegranate, 304 samples) further compounds model generalization. This study presents an end-to-end deep learning framework using VGG-16 and VGG-19 architectures trained on a novel, expert-verified dataset of 7,372 approved images spanning 22 disease/healthy classes across 5 fruit types, after on filed validation. The pipeline includes image preprocessing (CLAHE, Gaussian denoising, normalization), hybrid multi-feature extraction (CNN features, GLCM, LBP, Color Histograms), transfer learning with progressive fine-tuning, and a cascaded severity estimation module. Three algorithms are designed: (1) a Transfer Learning Classification Algorithm using VGG-16/VGG-19 backbone with softmax multi-class head, (2) a Hybrid Feature Fusion Algorithm combining CNN deep features with handcrafted descriptors for improved minority-class performance, and (3) a Cascaded Rule-CNN Severity Estimation Algorithm classifying disease progression into Healthy, Mild, Moderate, and Severe categories. VGG-19 achieved 96.1% overall accuracy, 95.1% precision, 94.5% recall, and a macro F1-score of 0.942, significantly outperforming VGG-16 (94.5% accuracy, F1: 0.918). Mango classification achieved the highest accuracy at 97.8%, while severity estimation reached 91.2% overall accuracy with the Mild category being the most challenging at 86.4%.The proposed system demonstrates that expert-verified, domain-specific datasets combined with transfer learning and hybrid feature fusion significantly advance the state of fruit disease detection for Indian agriculture. This framework provides a scalable, interpretable, and practically deployable solution for precision horticulture.

Plant phenotyping relevance

植物画像から病害状態と重症度を推定する深層学習手法、データセット、評価を中心的に開発しており、植物表現型計測の方法論的研究に該当する。

Code and data availability

The paper describes a novel expert-verified dataset of 7,372 fruit disease images and VGG-16/VGG-19 models, but contains no public deposit, availability statement, repository, or URL for the dataset, images, code, or trained models. No qualifying paper-specific public asset is present.

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