Iqbal, Anwar creator MS Robotics and Intelligent Machine Engineering text monographic 99p. Soft Copy 30cm Anwar Iqbal Cotton sustains the textile economies of 80+ countries with annual yield of 113 million bales, yet 30–60 % yield losses are still attributed to viral, bacterial and fungal diseases that remain undetected until manual field scouting. To address this critical issue, the use of Unmanned-aerial-vehicle (UAV) imagery combined with deep learning offers a scalable early-warning system, but existing models suffer from low early-stage accuracy, poor transferability and high computational cost. In this paper, we propose Cotton-Vision, a fully convolutional single-stage detector that synergizes three lightweight architectural innovations. The architectural changes include ghost bottleneck modules for 20 % parameter reduction, efficient channel-attention (ECA) for adaptive feature recalibration, and cross-stage partial (CSP) fusion for enriched gradient flow. The network is trained on local datasets of cotton collected from 7 different fields and comprised 2700+ drone images from local cotton farms in Punjab Province of Pakistan. Extensive augmentation (rotation, shear, exposure, saturation) yields 11704 training samples. On a held-out test set, Cotton-vision achieved a precision of 97.7%, a recall of 77.7%, a mAP@50 of 85.6%, and a mAP@50-95 of 76.6%. These results represent 10-20% improvement in precision, recall and mAP over the leading models, including YOLOv5, YOLOv8, YOLOv11, YOLOv12, RT-DETR and RF-DTR. Through a comprehensive ablation study, we demonstrate that each of our proposed architectural enhancements contributes to the model's superior performance with 12%-16% improvement from the base model. Our findings confirm that this synergy of lightweight and attention-based modules provides a robust and computationally viable solution for early cotton disease detection. 629.8 http://10.250.8.41:8080/xmlui/handle/123456789/56219 http://10.250.8.41:8080/xmlui/handle/123456789/56219