优化深度学习模型需要大量带标注图像，这一过程既耗时又昂贵。对于必须标注每个像素的语义分割模型而言尤其如此。缓解标注工作量的一种潜在策略是主动学习。主动学习有助于从大型未标注图像池中识别并选择信息量最高的图像。其基本前提是，与随机选择相比，这些被选中的图像能够更快提升模型性能，从而减少标注工作量。尽管主动学习已在 Cityscapes 等基准数据集上展现出良好结果，但其在农业领域的性能仍基本未被探索。本研究通过对三种基于主动学习的采集函数进行比较研究来弥补这一研究空白：Bayesian Active Learning by Disagreement（BALD，基于分歧的贝叶斯主动学习）、基于随机方法的 BALD（PowerBALD）以及 Random（随机）。这些采集函数在两个农业数据集上进行了测试：Sugarbeet 和 Corn-Weed，二者均包含三个语义类别：背景、作物和杂草。结果表明，主动学习（尤其是 PowerBALD）在两个数据集上都取得了高于 Random 采样的性能。但由于标准差相对较大，所观察到的差异很小；其部分原因在于图像冗余度高和类别不平衡。具体而言，两个数据集中超过 89% 的像素都属于背景类别。两个数据集上均未取得显著结果，这表明将主动学习应用于农业数据集仍需进一步研究，尤其是当这些数据集包含严重类别不平衡和冗余图像时。本文还提供了可能解决此类问题的建议和见解。
英文摘要：
Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which every pixel must be annotated. A potential strategy to mitigate annotation effort is active learning. Active learning facilitates the identification and selection of the most informative images from a large unlabelled pool. The underlying premise is that these selected images can improve the model's performance faster than random selection to reduce annotation effort. While active learning has demonstrated promising results on benchmark datasets like Cityscapes, its performance in the agricultural domain remains largely unexplored. This study addresses this research gap by conducting a comparative study of three active learning-based acquisition functions: Bayesian Active Learning by Disagreement (BALD), stochastic-based BALD (PowerBALD), and Random. The acquisition functions were tested on two agricultural datasets: Sugarbeet and Corn-Weed, both containing three semantic classes: background, crop and weed. Our results indicated that active learning, especially PowerBALD, yields a higher performance than Random sampling on both datasets. But due to the relatively large standard deviations, the differences observed were minimal; this was partly caused by high image redundancy and imbalanced classes. Specifically, more than 89\% of the pixels belonged to the background class on both datasets. The absence of significant results on both datasets indicates that further research is required for applying active learning on agricultural datasets, especially if they contain a high-class imbalance and redundant images. Recommendations and insights are provided in this paper to potentially resolve such issues.
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 environ…
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 …
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）
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…
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…
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）
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
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
Imperial College（帝国理工学院）；University of Sheffield（谢菲尔德大学）；University of Manchester（曼彻斯特大学）；University of Southampton（南安普顿大学）；Fudan University（复旦大学）；Iowa State University（爱荷华州立大学）
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）
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…
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…
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）
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）
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated…
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）
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）
EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics
Computer Engineering Department, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学计算机工程系）；SDAIA-KFUPM Joint Research Center on Artificial Intelligence, IRC for Intelligent Secure Systems and Department of Computer Engineering（人工智能联合研究中心、智能安全系统研究所及计算机工程系）
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…
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）
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…
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…
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…
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…
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…
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…
SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
ESPOL Polytechnic University（ESPOL多科技术大学）；Research Center for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））；University of Granada（格拉纳达大学）；Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）
