Semantic segmentation of microbial alterations based on SegFormer.

Autor:	Elmessery WM; Agricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh, Egypt.; Engineering Group, Centro de Investigaciones Biológicas del Noroeste, La Paz, Baja California Sur, Mexico., Maklakov DV; International Research Centre 'Biotechnologies of the Third Millennium', Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, Russia., El-Messery TM; International Research Centre 'Biotechnologies of the Third Millennium', Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, Russia., Baranenko DA; International Research Centre 'Biotechnologies of the Third Millennium', Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, Russia., Gutiérrez J; Engineering Group, Centro de Investigaciones Biológicas del Noroeste, La Paz, Baja California Sur, Mexico., Shams MY; Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt., El-Hafeez TA; Department of Computer Science, Faculty of Science, Minia University, Minia, Egypt.; Computer Science Unit, Deraya University, Minia University, Minia, Egypt., Elsayed S; Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Sadat City, Egypt., Alhag SK; Biology Department, College of Science and Arts, King Khalid University, Abha, Saudi Arabia., Moghanm FS; Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh, Egypt., Mulyukin MA; Institute of Natural and Technical Sciences, Surgut State University, Surgut, Russia., Petrova YY; Institute of Natural and Technical Sciences, Surgut State University, Surgut, Russia., Elwakeel AE; Agricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, Egypt.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2024 Jun 13; Vol. 15, pp. 1352935. Date of Electronic Publication: 2024 Jun 13 (Print Publication: 2024).
DOI:	10.3389/fpls.2024.1352935
Abstrakt:	Introduction: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation. Results: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general. Discussion: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Elmessery, Maklakov, El-Messery, Baranenko, Gutiérrez, Shams, El-Hafeez, Elsayed, Alhag, Moghanm, Mulyukin, Petrova and Elwakeel.)
Databáze:	MEDLINE
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