Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification

Abstract

Abstract Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturists are increasingly turning to advanced technology for more efficient disease control. This paper addresses this critical issue through an exploration of a deep learning-based approach to disease detection. Utilizing an optimized Convolutional Neural Network (E-CNN) architecture, the study concentrates on the early detection of prevalent leaf diseases in Apple, Corn, and Potato crops under various conditions. The research conducts a thorough performance analysis, emphasizing the impact of hyperparameters on plant disease detection across these three distinct crops. Multiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters. Additionally, the study investigates the influence of data augmentation on detection accuracy. The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. This research aims to pave the way for more efficient plant disease management and, ultimately, to enhance agricultural productivity in the face of mounting global challenges. To improve accessibility for farmers, the developed model seamlessly integrates with a mobile application, offering immediate results upon image upload or capture. In case of a detected disease, the application provides detailed information on the disease, its causes, and available treatment options.

Plant phenotyping relevance

植物葉の病徴を画像から検出・分類するCNN手法の開発と性能比較が研究の中心であり、植物の病害状態を直接推定しているため。

Code and data availability

The paper trains and evaluates its E-CNN plant disease detection models on the public PlantVillage Kaggle dataset (apple, corn, potato leaf images). No author-generated datasets, code, or models are released; the data availability statement says no datasets were generated or analysed. The only qualifying paper-specific