An improved YOLO11 model for accurate cotton growth-stage detection
Accurate crop detection in orchard environments is one of the primary challenges for agricultural systems today, as vision plays a crucial role in modern solutions for plant monitoring, crop harvesting, early disease detection, and analysing water and nutritional status, among other applications. In...
| Autores: | , , |
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| Formato: | capítulo de livro |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:upcommonspor::1eaa78772949c66acf6da9304a78d9f7 |
| Acesso em linha: | https://hdl.handle.net/2117/460908 https://dx.doi.org/10.3233/FAIA250601 |
| Access Level: | acceso abierto |
| Palavra-chave: | Deep learning Custom YOLO Cotton detection Cotton dataset Classificació INSPEC::Pattern recognition::Computer vision Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Resumo: | Accurate crop detection in orchard environments is one of the primary challenges for agricultural systems today, as vision plays a crucial role in modern solutions for plant monitoring, crop harvesting, early disease detection, and analysing water and nutritional status, among other applications. In our case, we want to detect cotton bolls and accurately label them in different stages of growth. To address this challenge, we present a custom YOLO11 model with a selection of state-of-the-art vision modules to improve cotton detection and reduce possible mismatches with a custom cotton dataset. Our approach achieves better detection accuracy with around 7M parameters and 16 GFLOPS, less than the official YOLO versions’ small to medium variants (8, 10, 11, 12). The final model will be used to count and monitor cotton plants in a greenhouse. |
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