Adaptive Strategies for Multi-Class Disease Detection in Azadirachta Indica Using Deep Learning with Few-Shot Adaptation (DL-FSA)
Received: 24 October 2025 | Revised: 10 November 2025 | Accepted: 21 November 2025 | Online: 9 February 2026
Corresponding author: H. A. Vidya
Abstract
These days, plant disease detection is a vital aspect because plants are the primary sources of nutrients for living organisms. Medicinal plants provide natural healing properties. Azadirachta indica (neem) is a versatile medicinal with numerous benefits, supporting skin health and immune function and offering wellness from root to leaf. However, it is susceptible to various diseases, making the identification and characterization of these threats essential to protect its invaluable contributions. The dataset for this study was collected in real time, and a few healthy neem samples from a public dataset were used for the healthy class. The study employs Generative Adversarial Networks (GANs) to generate synthetic dataset images. The dataset contains seven classes, one healthy class and six diseased classes: Alternaria, bacterial infection, defoliator, Dieback, irregular yellowing, and leaf blotch. The main objective of this work is to classify multiple diseases in Azadirachta indica (neem) leaves using a hybrid model, Deep Learning (DL) with Few-Shot Adaptation (DL-FSA), which integrates Few-Shot Learning (FSL), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Deep Neural Networks (DNNs). The hybrid model also employs a weighted average fusion technique to combine the probabilities from FSL and DNN to produce the final classification output. The model achieves an accuracy of 96% with a limited dataset, outperforming the baseline FSL model by 1.43%, demonstrating enhanced generalization and classification efficiency for neem leaf disease detection.
Keywords:
disease detection, Azadirachta indica, data augmentation, Generative Adversarial Network (GAN), Few-Shot Learning (FSL), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Deep Neural Network (DNN)Downloads
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Copyright (c) 2025 H. A. Vidya, M. S. Narasimha Murthy, A. Muthu Kumar
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