Implementation of EfficientNet-B0 CNN Model for Web-Based Strawberry Plant Disease Detection

Abstract

Strawberry production in Indonesia has high economic value but is often hindered by plant diseases that reduce yield quality and quantity. Manual disease identification requires time, cost, and expertise, making it inefficient for farmers. This study proposes a web-based strawberry disease detection system by applying a Convolutional Neural Network (CNN) model using the EfficientNet-B0 architecture. The dataset consists of leaf, fruit, and flower images of strawberries in both healthy and infected conditions. The research followed the CRISP-DM framework, including business understanding, data preparation, modeling, evaluation, and deployment. The model was trained using transfer learning and fine-tuning techniques, with evaluation conducted through a confusion matrix and K-Fold Cross Validation. Experimental results indicate that the EfficientNet-B0 model achieved an overall accuracy of approximately 95.2% and demonstrated stable performance in classifying various strawberry plant diseases. The model achieved perfect accuracy (100%) in several classes such as Healthy Leaf, Leaf Spot, and Healthy Flower, while maintaining high accuracy in other classes like Fruit (95.2%) and Anthracnose Fruit Rot (94.7%), confirming its effectiveness in capturing essential visual features for accurate disease classification. The deployment of the model into a website using the Streamlit framework enables users to upload strawberry images and obtain automatic, fast, and accurate disease detection results. This system is expected to provide a practical solution to help farmers improve productivity and minimize losses caused by plant diseases.

Plant phenotyping relevance

イチゴの葉・果実・花の画像から健全／感染状態や病害を推定するCNN手法を開発・評価し、Webシステムとして実装しており、植物状態の画像ベース表現型推定が中心である。

Code and data availability

The paper describes a strawberry disease detection system using EfficientNet-B0 and a 5,000-image dataset partly from Kaggle, but provides no authors' public code, model checkpoints, dataset URLs, or availability statements. The Kaggle dataset is mentioned without a link or identifier, and no repository or supplement (

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