A Hypothetical CNN-Based Framework for Disease Detection and Symptom Identification using Image-Based Datasets

S. Saleth Shanthi, Dr. M. P. Indra Gandhi

Fruit Disease Detection, Convolutional Neural Network (CNN), Deep Learning, Image Processing, Precision Agriculture, Symptom Identification, Grad-CAM, Artificial Intelligence, Computer Vision, Agricultural Informatics.

Fruit diseases impact global agricultural output, quality, and sustainability. Manual visual inspection by agricultural experts is time-consuming, subjective, and inaccurate, especially in large-scale farming. AI and DL have shown promise in automating plant disease diagnosis using image-based analysis. In particular, Convolutional Neural Networks (CNNs) can extract complicated visual characteristics and accurately diagnose disease signs. A CNN-based system for fruit disease diagnosis and symptom identification utilizing image-based datasets is proposed in this paper. The study aims to create an automated system that can efficiently classify healthy and unhealthy fruits and identify illness symptoms using visual interpretation. We use picture collections of healthy and damaged fruit samples from agricultural libraries. Data augmentation, normalization, and scaling were performed on images. Multiple convolutional, pooling, batch normalization, and fully connected layers in a TensorFlow-Keras CNN architecture learn disease-specific characteristics. Analyses include accuracy, precision, recall, F1-score, and confusion matrix analysis after Adam optimizer training. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizes classification-influencing symptom areas. According to the analysis, the suggested framework has 96.4% classification accuracy, precision, recall, and F1-score values above 95%. The Grad-CAM visualizations show disease-affected lesions, spots, discolouration, and rotting areas, improving model interpretation. Analysis shows that the CNN framework outperforms traditional machine learning and rudimentary deep learning models. In conclusion, the suggested framework for automated fruit disease diagnosis and symptom identification is accurate, dependable, and explainable. The study shows that CNN-based systems can aid precision agriculture, crop health monitoring, and disease management.

Received: 29 June 2026, Received in revised form: 26 July 2026, Accepted: 02 Aug 2026, Available online: 06 Aug 2026

10.22161/ijaers.138.1