Recognize and classify illnesses on tomato leaves using EfficientNet's Transfer Learning Approach with different size dataset

Abstract

Abstract This study focuses on the remarkable progress made by the agricultural sector in utilizing image processing techniques for early detection and classification of leaf plant diseases. Timely identification of diseases is crucial, but it often poses a challenge for the human eye to discern subtle differences. To address this issue, the researchers propose a novel approach that employs EfficientNet, a deep learning model, to accurately recognize various diseases affecting tomato plant leaves. Transfer learning is applied to three different datasets comprising 3000, 8000, and 10,000 images of diseased tomato leaves. The experimental results demonstrate impressive overall accuracies of 97.3%, 99.2%, and 99.5% when using 3000, 8000, and 10,000 images, respectively, for the detection of common tomato plant diseases. This research underscores the effectiveness of image processing and deep learning techniques in achieving precise and efficient detection of tomato leaf diseases. It significantly contributes to the advancement of precision agriculture and enhanced crop management practices.

Plant phenotyping relevance

トマト葉の病害状態を画像から認識・分類する深層学習手法が研究の中心であり、植物病害フェノタイピングに該当する。

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。

No evidence-backed public reproduction asset is currently recorded.