LDD: A Grape Diseases Dataset Detection and Instance Segmentation

The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control. To address the problem related to early disease detection and diagnosis on vines plants, a new dataset has been created with the goal of advancing the state-of-the-art of diseases recognition via instance segmentation approa ches. This was achieved by gathering images of leaves and clusters of grapes affected by diseases in their natural context. The dataset contains photos of 10 object types which include leaves and grapes with and without symptoms of the eight more common grape diseases, with a total of 17,706 labeled instances in 1,092 images. Multiple statistical measures are proposed in order to offer a complete view on the characteristics of the dataset. Preliminary results for the object detection and instance segmentation tasks reached by the models Mask R-CNN and R^3-CNN are provided as baseline, demonstrating that the procedure is able to reach promising results about th e objective of automatic diseases’ symptoms recognition.

实例分割（Instance Segmentation）作为经典目标检测（Object Detection）任务的延伸，在诸多领域均具备重要应用价值。以精准农业（precision agriculture）为例：通过自动识别植物器官及其伴随的潜在病害，可有效实现作物监测与病害防控的规模化与自动化。为解决葡萄植株早期病害检测与诊断的相关难题，本研究构建了全新数据集，旨在通过实例分割方法推进病害识别领域的现有顶尖技术水平。该数据集通过采集自然场景下受病害侵染的葡萄叶片与果穗图像构建而成，涵盖10类目标对象，包含带有8种常见葡萄病害症状与无病害症状的叶片及果穗，共计1092张图像，标注实例总量达17706个。研究团队同时提出多项统计指标，以全面展现该数据集的特性。作为基线基准，本文给出了Mask R-CNN与R³-CNN模型在目标检测与实例分割任务上的初步实验结果，证实该数据集可在自动病害症状识别任务中取得颇具潜力的实验效果。

搜集汇总

背景与挑战

背景概述

LDD是一个专注于葡萄病害检测和实例分割的数据集，包含1,092张图像和17,706个标注实例，覆盖8种常见葡萄病害的叶子和果串。该数据集旨在通过实例分割方法推进病害自动识别技术，并提供基线模型结果以验证其有效性，适用于精准农业中的作物监测应用。