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.

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。