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Papers/LDD: A Dataset for Grape Diseases Object Detection and Ins...

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

Leonardo Rossi, Marco Valenti, Sara Elisabetta Legler, Andrea Prati

2022-06-21SegmentationSemantic SegmentationInstance Segmentationobject-detectionObject Detection
PaperPDF

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.

Results

TaskDatasetMetricValueModel
Object DetectionLDDbox mAP22.7R^3-CNN
3DLDDbox mAP22.7R^3-CNN
Instance SegmentationLDDmask AP22.2R^3-CNN
2D ClassificationLDDbox mAP22.7R^3-CNN
2D Object DetectionLDDbox mAP22.7R^3-CNN
16kLDDbox mAP22.7R^3-CNN

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