Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this paper, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions --- a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.
Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for …
University of Lincoln（林肯大学）；Flanders Make（法兰德斯制造研究院）；Ghent University（根特大学）；Flanders Research Institute for Agriculture, Fisheries and Food (ILVO)（法兰德斯农业、渔业与食品研究所）；Swedish University of Agricultural Sciences（瑞典农业科学大学）
Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same sem…
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We i…
Weeds are a major threat to crops and are responsible for reducing crop yield worldwide. To mitigate their negative effect, it is advantageous to accurately identify them early in the season to prevent their spread throughout the field. Traditionally, farmers rely on manually scouting fields and applying herbicides for different weeds. However, it is easy to confuse between crops with weeds during the early growth stages. Recently, deep learning-based weed identification has become popular as deep learning relies on convolutional neural networks that are capable of learning important distingui…
Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aim…
From Seedling to Harvest: The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance Segmentation
Universidade Federal de Santa Maria（圣玛丽亚联邦大学）；University of Tsukuba（筑波大学）；Universidad Tecnológica del Uruguay（乌拉圭科技大学）；Wageningen University & Research（瓦赫宁根大学与研究中心）
Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devices
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, USA（农业与生物工程系，密苏里州立大学）；Sustainable Agriculture Research Unit, Mississippi State, MS, USA（可持续农业研究单位，密苏里州）；Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS, USA（植物与土壤科学系，密苏里州立大学）；Biosystems Engineering, North Dakota State University, Fargo, ND, USA（生物系统工程系，北达科塔州立大学）；NDSU Peltier Institute for the Advancement of Agricultural Technology, North Dakota State University, Fargo, ND, USA（NDSU裴特里农业技术进步研究所，北达科塔州立大学）
WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture
School of Computing（计算机学院）；Southern Illinois University Carbondale（南伊利诺伊大学卡本代尔分校）；Department of Plant Soils and Agricultural Systems（植物土壤与农业系统系）；School of Agricultural Sciences（农业科学学院）；School of Biological Sciences（生物科学学院）
PDT: Uav Target Detection Dataset for Pests and Diseases Tree
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算中心（国家超级计算济南中心））；Shandong Provincial Key Laboratory of Computer Networks（山东省计算机网络重点实验室）；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；SHANDONG SCICOM Information and Economy Research Institute Co., Ltd.（山东科信信息经济研究院有限公司）；Peking University（北京大学）
Centre de Developpement de Technologies Avancees（先进技术开发中心）
Computer vision techniques have attracted a great interest in precision agriculture, recently. The common goal of all computer vision-based precision agriculture tasks is to detect the objects of interest (e.g., crop, weed) and discriminating them from the background. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight, causing losses to crop yields. Weed detection and mapping is critical for site-specific weed management to reduce the cost of labor and impact of herbicides. This paper investigates the use of color and texture features for discriminat…
WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields
GFZ German Research Centre for Geosciences（GFZ德国地球科学研究中心）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Scientific Computing Center, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院科学计算中心）
Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants. In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded Convolutional Neural Network (CNN), Segnet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed da…
Lincoln Institute of Agri-food Technology, University of Lincoln（林肯大学林肯农业食品技术研究所）；Lincoln Centre for Autonomous Systems, University of Lincoln（林肯大学林肯自主系统中心）
The evolution of smaller, faster processors and cheaper digital storage mechanisms across the last 4-5 decades has vastly increased the opportunity to integrate intelligent technologies in a wide range of practical environments to address a broad spectrum of tasks. One exciting application domain for such technologies is precision agriculture, where the ability to integrate on-board machine vision with data-driven actuation means that farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. This makes sense both economically an…
CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture
Jeonbuk National University（全北国立大学）；Core Research Institute of Intelligent Robots, Jeonbuk National University（全北国立大学智能机器人核心研究所）；Universitas Udayana（乌达亚纳大学）；Rural Development Administration, National Institute of Crop Science（农村振兴厅国家作物科学研究所）
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
Smart weeding systems to perform plant-specific operations can contribute to the sustainability of agriculture and the environment. Despite monumental advances in autonomous robotic technologies for precision weed management in recent years, work on under-canopy weeding in fields is yet to be realized. A prerequisite of such systems is reliable detection and classification of weeds to avoid mistakenly spraying and, thus, damaging the surrounding plants. Real-time multi-class weed identification enables species-specific treatment of weeds and significantly reduces the amount of herbicide use. H…
Deep Learning Techniques for In-Crop Weed Identification: A Review
School of Computer Science, The University of Sydney（悉尼大学计算机学院）；School of Life and Environmental Sciences, The University of Sydney（悉尼大学生命与环境科学学院）；Australian Centre for Field Robotics, The University of Sydney（悉尼大学澳大利亚田间机器人中心）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；College of Mathematics and Computer Science, Wuhan Polytechnic University（武汉轻工大学数学与计算机学院）；Department of Computer Science and Engineering, Texas A&M University（德克萨斯农工大学计算机科学与工程系）；Citrus Research and Education Center, University of Florida（佛罗里达大学柑橘研究与教育中心）
Sapienza University of Rome（罗马第一大学）；Roma Tre University（罗马第三大学）；University of Padua（帕多瓦大学）；University of Basilicata（巴西利卡塔大学）
An effective perception system is a fundamental component for farming robots, as it enables them to properly perceive the surrounding environment and to carry out targeted operations. The most recent methods make use of state-of-the-art machine learning techniques to learn a valid model for the target task. However, those techniques need a large amount of labeled data for training. A recent approach to deal with this issue is data augmentation through Generative Adversarial Networks (GANs), where entire synthetic scenes are added to the training data, thus enlarging and diversifying their info…
INSA CVL（INSA中央卢瓦尔河谷校区）；University of Orleans（奥尔良大学）；PRISME EA 4229（PRISME EA 4229研究所）
Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agriculture is evolving to a more smart and precision farming to meet with the high yield and high quality crop production. Classification and recognition in Unmanned Aerial Vehicles (UAV) images are important phases for crop monitoring. Advances in deep learning models relying on Convolutional Neural Network (CNN) have achieved high performances in image classification in the agricultural domain. Despite the success of thi…
North Carolina State University（北卡罗来纳州立大学）；Colaberry Inc（Colaberry公司）；Iowa State University（爱荷华州立大学）
Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect damaged crop areas from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which was best suited for crop damage detection. Two modes of crop damage common in maize (corn) production were simulated: stalk…
Murdoch University（默多克大学）；Department of Primary Industries and Regional Development, Western Australia（西澳大利亚州初级产业与区域发展部）；University of South Australia（南澳大学）
Most weed species can adversely impact agricultural productivity by competing for nutrients required by high-value crops. Manual weeding is not practical for large cropping areas. Many studies have been undertaken to develop automatic weed management systems for agricultural crops. In this process, one of the major tasks is to recognise the weeds from images. However, weed recognition is a challenging task. It is because weed and crop plants can be similar in colour, texture and shape which can be exacerbated further by the imaging conditions, geographic or weather conditions when the images a…
University of Orléans（奥尔良大学）；INSA Centre Val de Loire（卢瓦尔河谷国立应用科学学院）
In modern agriculture, usually weeds control consists in spraying herbicides all over the agricultural field. This practice involves significant waste and cost of herbicide for farmers and environmental pollution. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial re…
Improved Crop and Weed Detection with Diverse Data Ensemble Learning
University of Regina（里贾纳大学）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿业大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in deep learning techniques have further enhanced the precision and reliability of aerial imagery based analysis. The capabilities to mount various kinds of sensors (RGB, spectral cameras) on UAV allows remote crop analysis applications such as vegetation classification and segmentation, crop counting, yield monitoring and prediction, crop mapping, weed detection, disease and nut…
Murdoch University（莫道克大学）；Centre for Crop and Food Innovation（作物与食品创新中心）；Food Futures Institute（食品未来研究所）；Department of Primary Industries and Regional Development, Western Australia（西澳大利亚州初级产业与区域发展部）；Centre for Sustainable Farming Systems（可持续农业系统中心）；Centre of Biosecurity and One Health, Harry Butler Institute（哈里·巴特勒研究所生物安全与同一健康中心）
The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos. DL techniques are now being used in many applications related to agriculture and farming. Automatic detection and classification of weeds can play an important role in weed management and so contribute to higher yields. Weed detection in crops from imagery is inherently a challenging problem because both weeds and crops have similar colours ('green-on-green'), and their shapes and texture can be very similar at the growth phase. Also, a crop in one s…
Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）
Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer beca…
BAWSeg: A UAV Multispectral Benchmark for Barley Weed Segmentation
Department of Computer Science and Software Engineering, The University of Western Australia（计算机科学与软件工程系，西澳大学）；Department of Primary Industries and Regional Development (DPIRD), Government of Western Australia（初级产业与地区发展部（DPIRD），西澳政府）
We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds common to the Canadian Prairies and many US states. The second dataset consists of over 540,000 images of plants imaged in farmland. All indoor plant images are labelled by species and we provide rich etadata on the level of individual images. This comprehensive database allows to filter the datasets under user-defined specifications such as for exam…
