EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics

2025-01-31

AI Summary

To address the bottlenecks of low weed identification accuracy, high energy consumption, and significant environmental risks in precision agriculture, this paper proposes EcoWeedNet—a lightweight deep learning model specifically designed for edge-deployed agricultural robots and consumer-grade farm machinery. Built upon an improved lightweight object detection architecture, EcoWeedNet innovatively integrates channel pruning with feature recalibration to optimize both efficiency and accuracy. On the CottonWeedDet12 benchmark, it achieves detection performance comparable to YOLOv4 while reducing model parameters by 95.79% (i.e., only 4.21% of YOLOv4’s parameter count) and computational complexity to just 6.59% of YOLOv4’s GFLOPs. Experimental results demonstrate substantially lower inference energy consumption, fulfilling requirements for low-power, low-cost, and carbon-conscious deployment. EcoWeedNet thus provides a practical, sustainable, and environmentally responsible technical foundation for intelligent, targeted weed management in modern agriculture.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Embodied AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Sustainability of Web economics

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 share essential resources with the crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The traditional methods employed to combat weeds include the usage of chemical herbicides and manual weed removal methods. However, these could damage the environment and pose health hazards. 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 and high computational expense. This work proposes EcoWeedNet, a novel model with enhanced weed detection performance without adding significant computational complexity, aligning with the goals of low-carbon agricultural practices. Additionally, our model is lightweight and optimal for deployment on ground-based consumer electronic agricultural vehicles and robots. The effectiveness of the proposed model is demonstrated through comprehensive experiments on the CottonWeedDet12 benchmark dataset reflecting real-world scenarios. EcoWeedNet achieves performance close to that of large models yet with much fewer parameters. (approximately 4.21% of the parameters and 6.59% of the GFLOPs of YOLOv4). This work contributes effectively to the development of automated weed detection methods for next-generation agricultural consumer electronics featuring lower energy consumption and lower carbon footprint. This work paves the way forward for sustainable agricultural consumer technologies.

Problem

• Weed Recognition
• Precision Agriculture
• Environmental Impact

Innovation

• EcoWeedNet
• Automated Weed Management
• Energy-efficient Agriculture