Publication
An improved YOLO11 model for accurate cotton growth-stage detection
Conference Article
Conference
Catalan Conference on Artificial Intelligence, poster session (CCIA-P)
Edition
2025
Pages
266-269
Doc link
http://dx.doi.org/10.3233/FAIA250601
File
Abstract
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.
Categories
computer vision, image classification, object detection.
Author keywords
Deep learning, Custom YOLO, Cotton detection, Cotton dataset
Scientific reference
G. González, S. Foix and G. Alenyà. An improved YOLO11 model for accurate cotton growth-stage detection, 2025 Catalan Conference on Artificial Intelligence, poster session, 2025, Valls, Spain, pp. 266-269, IOS Press.
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