LONG BEAN LEAF DISEASE IDENTIFICATION SYSTEM BASED ON MOBILE USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD

Abstract

Long beans (Vigna unguiculata subsp. sesquipedalis), have high nutritional value, besides long beans also have a significant role in the economy of farmers in Indonesia. However, the productivity of this plant is often hampered by various diseases that attack the leaves, which can result in a decrease in the quantity and quality of the harvest. This study has succeeded in developing a Convolutional Neural Network (CNN) model with the ResNet-50 architecture to identify six types of diseases in long bean leaves. The dataset used consists of 2,316 images, divided into training data (80%), validation (15%), and testing (5%). The ResNet-50 model, which consists of 50 layers, applies the transfer learning technique by not training the first 35 layers using a specific dataset, but utilizing weights from ImageNet. Training for 100 epochs produces high accuracy, namely 98.3% for training data, 98.4% for validation data, and 98.7% for testing data. Evaluation using Confusion Matrix, Precision, Recal and F1 Score shows very good performance without prediction errors. The final result of this research is a mobile-based software system that can diagnose diseases quickly and accurately, which can help farmers take appropriate action, and support sustainable agriculture in Indonesia.

Plant phenotyping relevance

長豆葉の画像から病害状態をCNNで識別する手法を開発・評価しており、植物病害表現型の取得が研究の中心です。

Code and data availability

The paper describes a CNN (ResNet-50) model for long bean leaf disease classification using a dataset of 2,316 images sourced from huggingface.co and Data.mendeley.com. However, no specific dataset URL, repository identifier, DOI, or author-deposited code/model checkpoint is provided anywhere in the supplied blocks. No

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