📝 SummarXiv

Abstract

Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds compete for essential resources with crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The adoption of automated computer vision technologies and ground agricultural consumer electronic vehicles in precision agriculture offers sustainable, low-carbon solutions. However, prior works suffer from issues such as low accuracy and precision, as well as high computational expense. This work proposes EcoWeedNet, a novel model that enhances weed detection performance without introducing significant computational complexity, aligning with the goals of low-carbon agricultural practices. The effectiveness of the proposed model is demonstrated through comprehensive experiments on the CottonWeedDet12 benchmark dataset, which reflects real-world scenarios. EcoWeedNet achieves performance comparable to that of large models (mAP@0.5 = 95.2%), yet with significantly fewer parameters (approximately 4.21% of the parameters of YOLOv4), lower computational complexity and better computational efficiency 6.59% of the GFLOPs of YOLOv4). These key findings indicate EcoWeedNet's deployability on low-power consumer hardware, lower energy consumption, and hence reduced carbon footprint, thereby emphasizing the application prospects of EcoWeedNet in next-generation sustainable agriculture. These findings provide the way forward for increased application of environmentally-friendly agricultural consumer technologies.