Klasifikasi Penyakit Tanaman Jeruk Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network Arsitektur EfficientNetV2-S

Abstract

The classification of citrus leaf diseases still largely relies on traditional assessment by farmers, which may lead to errors in identifying disease types. Previous studies have widely applied Convolutional Neural Networks (CNNs) for plant disease classification; however, most have utilized first-generation EfficientNet architectures, while the application of EfficientNetV2-S for citrus leaf disease classification remains relatively limited. Furthermore, the implementation of a progressive fine-tuning strategy on the EfficientNetV2-S architecture for this task has not been extensively investigated. Therefore, this study aims to implement the EfficientNetV2-S architecture for citrus leaf disease classification. The dataset used was the Citrus Leaves Prepared dataset from Kaggle, consisting of 596 images categorized into four classes: blackspot, canker, greening, and healthy. The data underwent preprocessing and image augmentation, including flipping, rotation, and zooming, before being divided into training, validation, and testing sets with a ratio of 70:10:20. The model was developed using a transfer learning approach combined with progressive fine-tuning. Experimental results demonstrated that the proposed model achieved a testing accuracy of 93.33% under the 100-epoch training scenario. With this level of accuracy, the model shows strong potential for implementation as an early detection system for citrus leaf diseases, assisting farmers in making timely and appropriate decisions to prevent crop failure.

Plant phenotyping relevance

柑橘葉の病徴を画像から分類するCNN手法の実装・評価が研究の中心であり、植物の病害状態を直接推定するため採用。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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