2023 journal article
Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images
PLANTS-BASEL, 12(21).
author keywords: rice blast; semi-supervised; soft labels; contrastive unpaired translation; unmanned aerial vehicle
TL;DR:
The findings demonstrate that the proposed model can accurately identify rice blast under field-scale conditions and is higher than those of common detection models (YOLO, Y OLACT, YOLACT++, Mask R-CNN, and Faster R- CNN).
(via Semantic Scholar)
open access via doi.org (publisher)
UN Sustainable Development Goal Categories
2. Zero Hunger
(OpenAlex)
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: December 4, 2023
Rice blast has caused major production losses in rice, and thus the early detection of rice blast plays a crucial role in global food security. In this study, a semi-supervised contrastive unpaired translation iterative network is specifically designed based on unmanned aerial vehicle (UAV) images for rice blast detection. It incorporates multiple critic contrastive unpaired translation networks to generate fake images with different disease levels through an iterative process of data augmentation. These generated fake images, along with real images, are then used to establish a detection network called RiceBlastYolo. Notably, the RiceBlastYolo model integrates an improved fpn and a general soft labeling approach. The results show that the detection precision of RiceBlastYolo is 99.51% under intersection over union (IOU0.5) conditions and the average precision is 98.75% under IOU0.5–0.9 conditions. The precision and recall rates are respectively 98.23% and 99.99%, which are higher than those of common detection models (YOLO, YOLACT, YOLACT++, Mask R-CNN, and Faster R-CNN). Additionally, external data also verified the ability of the model. The findings demonstrate that our proposed model can accurately identify rice blast under field-scale conditions.