Precision detection of grapevine downy and powdery mildew diseased leaves and fruits using enhanced ResNet50 with batch normalization

Abstract

Accurate detection and timely management of grapevine diseases, such as downy and powdery mildew, are essential for ensuring vineyard health and maximizing yield and quality. This study presents a novel approach using a ResNet50 model enhanced with batch normalization for precise identification and classification of grapevine leaf and fruit diseases. The dataset consists of 1,226 images categorized into five classes: Downy mildew diseased fruits (70 training, 18 testing), Downy mildew diseased leaves (534 training, 134 testing), Healthy leaves (183 training, 46 testing), Powdery mildew diseased fruits (93 training, 23 testing), and Powdery mildew diseased leaves (100 training, 25 testing). The model achieved an impressive accuracy of 95% in distinguishing between healthy and diseased grapevine leaves during rigorous evaluation and validation phases. Evaluation metrics—precision (94%), recall (96%), and F₁-score (95%)—highlight the model’s effectiveness compared to conventional methods. This research demonstrates the feasibility and superiority of deep learning in vineyard disease management, emphasizing its potential to revolutionize viticulture practices through automated, real-time disease detection and monitoring. These findings contribute to advancing agricultural sustainability and productivity through innovative technology applications in plant pathology.

Plant phenotyping relevance

ブドウ葉・果実の病害状態を画像から分類する深層学習手法を開発・評価しており、植物病害表現型の取得が研究の中心である。

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