Early Detection of Grapevine ( Vitis vinifera ) Downy Mildew ( Peronospora ) and Diurnal Variations Using Thermal Imaging.

Abstract

Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew ( Peronospora ) on grapevine ( Vitis vinifera ) leaves at early stages of development using thermal imaging technology and to determine the best time during the day for image acquisition. In controlled experiments, 1587 thermal images of grapevines grown in a greenhouse were acquired around midday, before inoculation, 1, 2, 4, 5, 6, and 7 days after an inoculation. In addition, images of healthy and infected leaves were acquired at seven different times during the day between 7:00 a.m. and 4:30 p.m. Leaves were segmented using the active contour algorithm. Twelve features were derived from the leaf mask and from meteorological measurements. Stepwise logistic regression revealed five significant features used in five classification models. Performance was evaluated using K-folds cross-validation. The support vector machine model produced the best classification accuracy of 81.6%, F1 score of 77.5% and area under the curve (AUC) of 0.874. Acquiring images in the morning between 10:40 a.m. and 11:30 a.m. resulted in 80.7% accuracy, 80.5% F1 score, and 0.895 AUC.

Plant phenotyping relevance

熱画像と画像解析・分類モデルを用いてブドウ葉の病害状態を早期推定する手法を開発・評価しており、植物病害表現型の取得が中心です。

Code and data availability

The paper's Data Availability Statement explicitly deposits the authors' phenotyping datasets (thermal-image-derived leaf temperature features, meteorological measurements, and disease severity labels) as Excel files in two public GitHub repositories, both listed in allowed_urls. These directly reproduce the paper's 1,