A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI).

Abstract

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

Plant phenotyping relevance

トマト葉の画像から病害状態を推定する深層学習・スマートフォンシステムの開発と検証が研究の中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The authors publicly deposited their main tomato leaf image dataset and smartphone application (.apk) on Zenodo, and deployed a live web application for tomato leaf disease detection on Streamlit. Both are paper-specific, public, and actionable. The Kaggle and Mendeley datasets are third-party source datasets cited as输