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

• URL: http://arxiv.org/abs/2502.00205v2
• Date: Wed, 07 May 2025 11:40:20 GMT
• Title: EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics
• Authors: Omar H. Khater, Abdul Jabbar Siddiqui, M. Shamim Hossain, Aiman El-Maleh,
• Abstract summary: EcoWeedNet is a novel model that enhances weed detection performance without introducing significant computational complexity.<n>Results indicate EcoWeedNet's deployability on low-power consumer hardware, lower energy consumption, and hence reduced carbon footprint.
• Score: 7.900421718750201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture [0.0]
  This work presents an optimized, low-power edge AI system for weeds detection based on the YOLOv8n object detector deployed on the STM32U575ZI microcontroller.<n>Weeds significantly reduce crop yields worldwide and pose major challenges to sustainable agriculture.
  arXiv  Detail & Related papers  (2025-11-11T08:55:58Z)
• AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock [77.95897723270453]
  Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population.<n> Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI)<n>This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques, and recent vision-language foundation models.
  arXiv  Detail & Related papers  (2025-07-29T17:59:48Z)
• How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference [0.0]
  This paper introduces a novel infrastructure-aware benchmarking framework for quantifying the environmental footprint of AI inference across 30 state-of-the-art models as deployed in commercial data centers.<n>Our results show that o3 and DeepSeek-R1 emerge as the most energy-intensive models, consuming over 33 Wh per long prompt, more than 70 times the consumption of GPT-4.1 nano, and that Claude-3.7 Sonnet ranks highest in eco-efficiency.<n>These findings illustrate a growing paradox: Although AI is becoming cheaper and faster, its global adoption drives disproportionate resource consumption.
  arXiv  Detail & Related papers  (2025-05-14T17:47:00Z)
• Internet of Things-Based Smart Precision Farming in Soilless Agriculture:Opportunities and Challenges for Global Food Security [3.46887201928427]
  The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution. This paper explores the opportunities and challenges of IoT-based soilless farming.
  arXiv  Detail & Related papers  (2025-03-15T03:40:32Z)
• Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management [3.4161054453684705]
  This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops.<n>FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset.
  arXiv  Detail & Related papers  (2025-03-11T12:00:56Z)
• Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
  We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
  arXiv  Detail & Related papers  (2024-11-07T22:29:05Z)
• The unrealized potential of agroforestry for an emissions-intensive agricultural commodity [48.652015514785546]
  We use machine learning to generate estimates of shade-tree cover and carbon stocks across a West African region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually.
  arXiv  Detail & Related papers  (2024-10-28T10:02:32Z)
• BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform [2.3961612657966946]
  This article presents the recent advancements in weed management algorithms and the real-world performance of bbot at the University of Bonn's Klein-Altendorf campus. We present a novel Rolling-view observation model for the BonnBot-Is weed monitoring section which leads to an average absolute weeding performance enhancement of $3.4%$. For the first time, we show how precision weeding robots could consider bio-diversity-aware concerns in challenging weeding scenarios.
  arXiv  Detail & Related papers  (2024-05-15T06:23:59Z)
• WeedScout: Real-Time Autonomous blackgrass Classification and Mapping using dedicated hardware [1.1859244973229535]
  The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM) The system processes live image feeds, infers blackgrass density, and covers two stages of maturation. By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices.
  arXiv  Detail & Related papers  (2024-05-12T18:04:41Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning [0.0]
  This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage.
  arXiv  Detail & Related papers  (2024-02-14T09:07:00Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)
• Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI [47.130004596434816]
  In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
  arXiv  Detail & Related papers  (2022-07-31T09:34:53Z)
• IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control [8.344829083719857]
  We propose a smart agriculture solution, namely iGrow. We use IoT and cloud computing technologies to measure, collect, and manage growing data. Our solution significantly increases production (commercially sellable fruits) and net profit.
  arXiv  Detail & Related papers  (2021-07-06T11:35:50Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
  Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming. Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
  arXiv  Detail & Related papers  (2020-07-07T12:44:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.