Turkish Journal of Agriculture and Forestry
Abstract
Unmanned aerial vehicles (UAVs) have become essential tools in precision agriculture, facilitating large-scale monitoring of crop health, weed proliferation, and overall field conditions. However, conventional single-task recognition models encounter significant challenges when implemented on resource-constrained UAV platforms, particularly in detecting densely distributed small objects, managing scale variations, and addressing occlusions. To address these challenges, this paper proposes a channel attention-based adaptive fusion network (CNAF-Net) specifically designed for agricultural scenarios. CNAF-Net utilizes MobileNetV3 for hierarchical feature extraction and integrates a channel attention-guided adaptive fusion mechanism that dynamically combines multiscale features across detection, classification, and segmentation tasks. Experiments conducted on the WeedMap dataset demonstrate that CNAF Net consistently surpasses state-of-the-art lightweight models, including YOLOv5-s and Lite-FPN-SSD, in terms of precision, recall, F1-score, and mean average precision. In addition to quantitative analyses, visualization experiments further confirm the model’s applicability to agricultural scenarios. Specifically, CNAF-Net generates canopy coverage and leaf area index heatmaps for growth monitoring and produces crop–weed–soil segmentation masks that enable fine-grained analyses of vegetation density and interspecies competition. Ablation studies highlight the contribution of the adaptive fusion module to enhanced feature selectivity, convergence, and robustness. Overall, CNAF-Net offers an efficient and scalable solution for UAV-based agricultural monitoring. Its compact architecture and robust generalization capability enable real-time deployment on edge devices, providing practical support for weed detection, crop growth assessment, and sustainable precision agriculture.
DOI
10.55730/1300-011X.3499
Keywords
UAV-based agricultural monitoring, multitask learning, weed detection, crop monitoring, adaptive feature fusion
First Page
729
Last Page
741
Publisher
The Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
YUE, X, & WAN, Z (2026). A lightweight multitask adaptive fusion network with channel attention for UAV-based crop detection and classification. Turkish Journal of Agriculture and Forestry 50 (5): 729-741. https://doi.org/10.55730/1300-011X.3499
