Plant Disease Management: A Fine-Tuned Enhanced CNN Approach with Mobile App Integration for Early Detection and Classification

Abstract

Abstract Farmers face a daunting challenge in meeting the escalating demands of a rapidly growing population for agricultural products, while plant diseases continue to exact a devastating toll on food production. Despite investing billions of dollars in disease management, agriculturists often struggle to achieve effective disease control without the support of advanced technology. The article explores a deep learning-based approach for disease detection. Specifically, it employs a Convolutional Neural Network (CNN) architecture for the detection. For the automated detection of plant disease, using plant images. This paper presents a new model for the early detection of plant detection based on processing plant images. And compare the in-depth performance analysis of hyper parameters in the context of plant disease detection by focusing on three distinct crops: (Apple, Corn, and Potato). Moreover, the data augmentation impact is analyzed. To enhance accessibility for farmers, our model is seamlessly integrated with a mobile application. The experimental results show the efficiency of our fine-tuned enhanced CNN model (E-CNN) achieving 98.17% accuracy on fungal classes. This research endeavors to pave the way for more effective plant disease management and ultimately to improve agricultural productivity in the face of mounting global challenges.

Plant phenotyping relevance

植物画像から病害状態を推定するCNNを開発・評価し、ハイパーパラメータとデータ拡張の性能分析も行っているため、病害フェノタイピング手法が中心です。

Code and data availability

The paper's plant disease detection experiments are built on the public PlantVillage image dataset, which the authors explicitly cite with a Kaggle URL. No author-generated code, trained model checkpoints, or supplementary data deposits are mentioned.