COTONET: A custom cotton detection algorithm based on YOLO11 for stage of growth cotton boll detection
Guillem Gonz\'alez, Guillem Aleny\`a, Sergi Foix

TL;DR
COTONET is an advanced YOLO-based model with attention mechanisms designed to accurately detect cotton bolls at various growth stages, facilitating automated harvesting while maintaining fiber quality.
Contribution
The paper introduces COTONET, a novel YOLO11-based architecture with integrated attention modules and architectural enhancements for improved cotton boll detection.
Findings
COTONET achieves a mAP50 of 81.1%.
It outperforms standard YOLO models in cotton detection accuracy.
Designed for low-resource edge devices.
Abstract
Cotton harvesting is a critical phase where cotton capsules are physically manipulated and can lead to fibre degradation. To maintain the highest quality, harvesting methods must emulate delicate manual grasping, to preserve cotton's intrinsic properties. Automating this process requires systems capable of recognising cotton capsules across various phenological stages. To address this challenge, we propose COTONET, an enhanced custom YOLO11 model tailored with attention mechanisms to improve the detection of difficult instances. The architecture incorporates gradients in non-learnable operations to enhance shape and feature extraction. Key architectural modifications include: the replacement of convolutional blocks with Squeeze-and-Exitation blocks, a redesigned backbone integrating attention mechanisms, and the substitution of standard upsampling operations for Content Aware Reassembly…
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Taxonomy
TopicsSmart Agriculture and AI · Advanced Neural Network Applications · Research in Cotton Cultivation
