Volume no :
1 |
Issue no :
2
Article Type :
Scholarly Article
Author :
Venkat Sai, Purushottam,P. Aparna
Published Date :
March, 2025
Publisher :
INTERNATIONAL JOURNAL OF ENGINEERING INNOVATIONS AND MANAGEMENT STRATEGIES
Page No: 1 - 13
Abstract : Plant diseases significantly impact global agricultural productivity, making early and accurate detection essential for effective crop management. This paper presents PlantDet, a robust multi-model ensemble framework based on deep learning, designed to improve the accuracy and generalizability of plant disease detection across diverse crops and environmental conditions. Unlike traditional single-model approaches that often suffer from overfitting and limited feature representation, PlantDet integrates multiple state-of-the-art convolutional neural networks (CNNs)—including ResNet50, EfficientNet-B3, and DenseNet121—within an ensemble architecture. The framework employs a two-stage training process: initially training each base model independently on a curated dataset of diseased plant leaf images, and then combining their outputs using a gradient boosting meta-classifier to capture complementary predictive patterns. Extensive evaluations on benchmark datasets such as PlantVillage and real-world agricultural images show that PlantDet consistently outperforms individual models and standard ensemble baselines, achieving a top-1 accuracy of 98.7% on PlantVillage while maintaining high performance under noisy, imbalanced conditions. The model also incorporates Grad-CAM-based explainability features to highlight disease-affected regions, thereby increasing interpretability for domain experts. Designed for both cloud and edge deployment, PlantDet is scalable, adaptable to various crops, and well-suited for real-time applications. By effectively leveraging ensemble learning and deep feature extraction, PlantDet offers a reliable and interpretable solution for automated plant disease detection, supporting precision agriculture and contributing to sustainable farming practices.
Keyword Plant disease detection; Deep learning; Convolutional neural networks (CNNs); Ensemble learning; Precision agriculture; Image classification.
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