LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation

TL;DR

This paper introduces a new dataset for grape disease detection and segmentation, enabling improved automatic recognition of plant diseases to support precision agriculture.

Contribution

The creation of a comprehensive grape disease dataset with labeled images and baseline results for object detection and segmentation tasks.

Findings

Baseline models achieved promising detection accuracy.

The dataset includes 17,706 labeled instances across 1,092 images.

Statistical measures characterize dataset diversity and complexity.

Abstract

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 approaches. 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 the objective of automatic diseases' symptoms recognition.