A deep learning-based approach for the detection of cucumber diseases.

Abstract

Cucumbers play a significant role as a greenhouse crop globally. In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditions, they are highly vulnerable to various diseases. Therefore the accurate detection of cucumber diseases is essential for maintaining crop quality and ensuring food security. Traditional methods, reliant on human inspection, are prone to errors, especially in the early stages of disease progression. Based on a VGG19 architecture, this paper uses an innovative transfer learning approach for detecting and classifying cucumber diseases, showing the applicability of artificial intelligence in this area. The model effectively distinguishes between healthy and diseased cucumber images, including Anthracnose, Bacterial Wilt, Belly Rot, Downy Mildew, Fresh Cucumber, Fresh Leaf, Pythium Fruit Rot, and Gummy Stem Blight. Using this novel approach, a balanced accuracy of 97.66% on unseen test data is achieved, compared to a balanced accuracy of 93.87% obtained with the conventional transfer learning approach, where fine-tuning is employed. This result sets a new benchmark within the dataset, highlighting the potential of deep learning techniques in agricultural disease detection. By enabling early disease diagnosis and informed agricultural management, this research contributes to enhancing crop productivity and sustainability.

Plant phenotyping relevance

キュウリ画像から健全・罹病状態を深層学習で分類する手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。未見データで性能評価も行っている。

Code and data availability

The paper uses a pre-existing public dataset from Sultana et al. (cited prior work, doi:10.1016/j.dib.2023.109320 on Mendeley), not a paper-specific deposit by these authors. No author analysis code, trained model checkpoints, or paper-specific data repository with an actionable URL is provided; the allowed URLs are P1

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