期刊论文

Zhou, GX

英文

植物表型组学

2643-6515

2023

5

0049

10.34133/plantphenomics.0049

We are grateful to all members of the Hunan Academy of Agricultural Sciences for their advice and assistance in the course of this research. The language of our manuscript has been refined and polished by Elsevier Language Editing Services (Serial number: LE-249881-01D60B7F7A18). This work was supported by the Scientific Research Project of Education Department of Hunan Province (Grant No. 21A0179); in part by the Changsha Municipal Natural Science Foundation (Grant No. kq2014160); in part by the National Natural Science Fund project (Grant No. 62276276); in part by the Natural Science Foundation of China (Grant No. 61902436); and in part by Hunan Key Laboratory of Intelligent Logistics Technology (2019TP1015). Y.D.: Conceptualization, methodology, writing (original draft), and software. H.X.: Data curation, charting, and investigation. G.Z.: Validation and project administration. A.C.: Supervision and funding acquisition. Y.W.: Supervision and resources. L.L.: Writing (review and editing). Y.H.: Visualization and resources. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module o...