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.