DeepMangoNet: An Intelligent ResNet50-Based Framework for Automated Mango Leaf Disease Diagnosis, Severity Assessment, and Interactive Analytic
TL;DR
This work introduces a deployable artificial intelligence system for automatic mango leaf disease recognition using deep transfer learning integrated with an interactive analytics dashboard and indicates that transfer learning can be trained efficiently and has a good predictive power.
Abstract
Timely detection of plant diseases is critical for sustaining agricultural production and reducing economic loss. Mango crops are prone to several leaf infections that are difficult to diagnose without expert supervision. This work introduces a deployable artificial intelligence system for automatic mango leaf disease recognition using deep transfer learning integrated with an interactive analytics dashboard. A pretrained ResNet50 convolutional neural network is used as a feature extractor, followed by customized fully connected layers for multi-class classification. The trained model is integrated into a web-based interface using Streamlit, so users canupload images, obtainpredictions immediately, and view confidence-based analysis. The proposed framework supports 8 disease categories. The proposed framework outputs interpretable results such as severity estimation and probability distribution visualization. The experiment results indicate that transfer learning can be trained efficiently and has a good predictive power. It provides a practical way to integrate deep learning solutions to precision agriculture environments.