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 [6] and R3 -CNN [10] are provided as baseline, demonstrating that the procedure is able to reach promising results about the objective of automatic diseases’ symptoms recognition.

LDD: A Grape Diseases Dataset Detection and Instance Segmentation / Rossi, L.; Valenti, M.; Legler, S. E.; Prati, A.. - 13232:(2022), pp. 383-393. ((Intervento presentato al convegno 21st International Conference on Image Analysis and Processing, ICIAP 2022 tenutosi a ita nel 2022 [10.1007/978-3-031-06430-2_32].

LDD: A Grape Diseases Dataset Detection and Instance Segmentation

Rossi L.;Prati A.
2022

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 [6] and R3 -CNN [10] are provided as baseline, demonstrating that the procedure is able to reach promising results about the objective of automatic diseases’ symptoms recognition.
978-3-031-06429-6
978-3-031-06430-2
LDD: A Grape Diseases Dataset Detection and Instance Segmentation / Rossi, L.; Valenti, M.; Legler, S. E.; Prati, A.. - 13232:(2022), pp. 383-393. ((Intervento presentato al convegno 21st International Conference on Image Analysis and Processing, ICIAP 2022 tenutosi a ita nel 2022 [10.1007/978-3-031-06430-2_32].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2933181
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact