Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different set of weed species present within them. In order to overcome this limitation, this paper works towards developing a clustering approach to weed scouting which can be utilized in any field without the need for prior species knowledge. We demonstrate our system using challenging data collected in the field from an agricultural robotics platform. We show that considerable improvements can be made by (i) learning low-dimensional (bottleneck) features using a deep convolutional neural network to represent plants in general and (ii) tying views of the same area (plant) together. Deploying this algorithm on in-field data collected by AgBotII, we are able to successfully cluster cotton plants from grasses without prior knowledge or training for the specific plants in the field.
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 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…
Michigan State University（密歇根州立大学）；Mississippi State University（密西西比州立大学）
Weed management plays an important role in many modern agricultural applications. Conventional weed control methods mainly rely on chemical herbicides or hand weeding, which are often cost-ineffective, environmentally unfriendly, or even posing a threat to food safety and human health. Recently, automated/robotic weeding using machine vision systems has seen increased research attention with its potential for precise and individualized weed treatment. However, dedicated, large-scale, and labeled weed image datasets are required to develop robust and effective weed identification systems but th…
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）
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…
Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technology is expensive and also prone to failure (e.g. through lack of coverage). As such, autonomous navigation through sensors that can interpret their environment (such as cameras) is important to achieve the goal of autonomy in agriculture. In this paper, we introduce a purely vision-based navigation scheme that is able to reliably guide the robot through row-crop fields without m…
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）
Computer Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM)（计算机工程系，国王法赫德石油与矿物大学）；SDAIA-KFUPM Joint Research Center on Artificial Intelligence, IRC for Intelligent Secure Systems and Department of Computer Engineering, KFUPM（SDAIA-KFUPM人工智能联合研究中心，智能安全系统研究院及计算机工程系，KFUPM）；Center for Intelligent Secure Systems and the Department of Computer Engineering, King Fahd University of Petroleum & Minerals（智能安全系统中心及计算机工程系，国王法赫德石油与矿物大学）；Research Chair of Pervasive and Mobile Computing, Department of Software Engineering, College of Computer and Information Sciences, King Saud University（普及与移动计算研究主席，软件工程系，计算机与信息科学学院，国王沙特大学）
本文提出TinyEcoWeedNet，通过结构化剪枝、量化感知训练和TensorRT加速，在Jetson Orin Nano上实现184 FPS实时杂草检测，模型缩小68.5%，并在CottonWeedDet12上以85.9% mAP50超越YOLO11n和YOLO12n。
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…
Design of a six wheel suspension and a three-axis linear actuation mechanism for a laser weeding robot
Robotics and Intelligent Systems Engineering (RISE) Lab（机器人与智能系统工程实验室）；Department of Robotics and Artificial Intelligence（机器人与人工智能系）；School of Mechanical and Manufacturing Engineering (SMME)（机械与制造工程学院）；National University of Sciences and Technology (NUST)（国立科学与技术大学）
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…
Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural image…
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…
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）
WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification
Department of Agronomy, Iowa State University, Iowa, USA（农业学系，爱荷华州立大学）；Department of Computer Science, Iowa State University, Iowa, USA（计算机科学系，爱荷华州立大学）；Data Science Institute, University of Arizona, Arizona, USA（数据科学研究所，亚利桑那大学）；Department of Mechanical Engineering, Iowa State University, Iowa, USA（机械工程系，爱荷华州立大学）；Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Iowa, USA（植物病理学、昆虫学和微生物学系，爱荷华州立大学）
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…
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）
University of Bonn（波恩大学）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；La Sapienza University of Rome（罗马第一大学）
Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic manner. Semantic perception mostly relies on deep learning using supervised approaches, which require time and qualified workers to label fairly large amounts of data. In this paper, we look into the problem of reducing the amount of labels without compromising the final segmentation performance. Fo…
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
Center for Robotics, University of Bonn（波恩大学机器人中心）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究院）
Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring. At the same time, they move in a continuously changing, semi-structured field environment, in which features can hardly be found and reproduced at a later time. Challenges for Lidar and visual detection systems stem from the fact that plants can be very small, overlapping and have a steadily changing appearance. Therefore, a popular way to localize vehicles with high accuracy is based on ex- pensive global navigation satellite systems and not on natural l…
University College Dublin（都柏林大学学院）；Dublin City University（都柏林城市大学）；Insight Centre for Data Analytics（洞察数据分析中心）
The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clover biomass yield enables smart decisions in optimizing fertilization and seeding density, resulting in increased productivity and positive environmental impact. Grass and clover are usually planted together, since clover is a nitrogen-fixing plant that brings nutrients to the soil. Adjusting the right percentages of clover and grass in a field reduces the need for external fertilization. Existing approaches for estimating the grass-clover composition of a field are expensive and time consuming - r…
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Center for Robotics, University of Bonn（柏林洪堡大学机器人中心）；Institute of Sugar Beet Research（甘蔗研究院）；Lamarr Institute for Machine Learning and Artificial Intelligence（Lamarr人工智能与机器学习研究所）
