期刊论文

Zhou, GX

英文

植物表型组学

2643-6515

2023

2023

0042

10.34133/plantphenomics.0042

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-249735-FE04C6824BA6). This work was supported by the Scientific Research Project of the 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). Z.T.: Conceptualization, Methodology, Writing—original draft, and Software. X.H.: 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 competing interests.

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Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf's edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image...