A Comparative Analysis of Grape Plant Leaf Disease Detection - Methods and Challenges

Abstract

Grape leaf diseases significantly threaten global viticulture, leading to substantial declines in grape yield. These diseases, attributed to various factors such as bacteria, viruses, fungi, and pests like mealybugs, can propagate through diverse means, including wind, rain, and human activities. They identify symptoms such as leaf spots and yellowing for effective disease management. Current strategies for disease control involve biological agents and chemical pesticides, with challenges arising from the lack of treatment options for certain viral infections like grapevine fan leaf and rupestris stem pitting. This review explores the myriad methods employed for grape leaf disease detection, emphasizing the use of emerging technologies to address these challenges. Integrating IoT (Internet of Things) and image analysis has demonstrated efficacy in disease detection, enabling farmers to make informed decisions promptly. While fungicides prove effective against diseases like downy mildew and powdery mildew, viral infections present persistent challenges. Technological interventions, particularly IoT and image processing, offer promising avenues for identifying grape leaf diseases, providing farmers with timely and valuable information to enhance disease management strategies. The review underscores the importance of continued research and technological innovation to address the complexities of grape leaf diseases and ensure sustainable grape cultivation.

Plant phenotyping relevance

ブドウ葉の病徴を対象に、画像解析やIoTを用いた病害検出手法をレビューしており、植物の病害状態を観測・推定する方法が中心である。

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