The 1st Agriculture-Vision Challenge: Methods and Results
UIUC（伊利诺伊大学厄巴纳-香槟分校）；Intelinair；University of Oregon（俄勒冈大学）；Agency for Defense Development（国防发展局）；DGIST（大邱庆北科学技术院）；Norwegian Computing Center（挪威计算中心）；UiT The Arctic University of Norway（特罗姆瑟大学－挪威北极大学）；Tongji University（同济大学）；Stanford University（斯坦福大学）；Chegg, Inc.（Chegg公司）；Chonnam National University（全南大学）；Chosun University（朝鲜大学）；SGGS Institute of Engineering and Technology（SGGS工程技术学院）；Tsinghua University（清华大学）
Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunn… 展开作者
Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Junhee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt B. Salberg, Alexandre Barbosa, Rodrigo Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Ng, Van Thong Huynh, Soo-Hyung Kim, In-Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay Talbar, Jianyu Tang
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here.
UIUC（伊利诺伊大学厄巴纳-香槟分校）；Intelinair；University of Oregon（俄勒冈大学）
The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremel…
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…
Early, precise detection of nutrient deficiency stress (NDS) has key economic as well as environmental impact; precision application of chemicals in place of blanket application reduces operational costs for the growers while reducing the amount of chemicals which may enter the environment unnecessarily. Furthermore, earlier treatment reduces the amount of loss and therefore boosts crop production during a given season. With this in mind, we collect sequences of high-resolution aerial imagery and construct semantic segmentation models to detect and predict NDS across the field. Our work sits a…
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）
PAII Inc.（PAII公司）；Ping An Bank Co., Ltd（平安银行股份有限公司）
The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplishing the 2nd place in this challenge.
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）
Electrical and Electronics Engineering Institute（电气与电子工程学院）；University of the Philippines Diliman（菲律宾大学迪利曼分校）
The inclusion of Computer Vision and Deep Learning technologies in Agriculture aims to increase the harvest quality, and productivity of farmers. During postharvest, the export market and quality evaluation are affected by assorting of fruits and vegetables. In particular, apples are susceptible to a wide range of defects that can occur during harvesting or/and during the post-harvesting period. This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy ap…
This is the proceedings of the Computer Vision for Agriculture (CV4A) Workshop that was held in conjunction with the International Conference on Learning Representations (ICLR) 2020. The Computer Vision for Agriculture (CV4A) 2020 workshop was scheduled to be held in Addis Ababa, Ethiopia, on April 26th, 2020. It was held virtually that same day due to the COVID-19 pandemic. The workshop was held in conjunction with the International Conference on Learning Representations (ICLR) 2020.
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and t…
Indian Institute of Technology（印度理工学院）；The University of Tokyo（东京大学）
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant pheno…
DAS-SK: An Adaptive Model Integrating Dual Atrous Separable and Selective Kernel CNN for Agriculture Semantic Segmentation
Electrical & Computer Engineering Department of Lakehead University（拉夫莱德大学电气与计算机工程系）；Faculty of Software Engineering of Lakehead University（拉夫莱德大学软件工程系）；Faculty of Applied Science of the University of Alabama（阿拉巴马大学应用科学系）；School of Computer Science Engineering and Information Systems, Vellore Institute of Technology（维洛雷理工学院计算机科学工程与信息系统学院）
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture
Department of Electrical and Computer Engineering, The University of Alabama（电气与计算机工程系，阿拉巴马大学）；Electrical and Computer Engineering, Michigan State University（电气与计算机工程，密歇根州立大学）；Agricultural and Biological Engineering, Mississippi State University（农业与生物工程，密苏里州立大学）；Department of Computer Science, University of Alabama（计算机科学系，阿拉巴马大学）；USDA-ARS Genetics and Sustainbale Agriculture（美国农业部ARS基因与可持续农业）
Mississippi State University（密西西比州立大学）；Michigan State University（密歇根州立大学）；United States Department of Agriculture, Agricultural Research Service（美国农业部农业研究局）
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets, however, are often difficult to obtain to fuel the development of advanced, high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, data augmentation plays a crucial role in b…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Intelinair
A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is particularly true for semantic segmentation tasks, which are much less common in the remote sensing domain because of the incredible difficulty in collecting precise, accurate, pixel-level annotations at scale. Recent efforts have addressed these challenges both through the creation of supervised datasets as well as the application of self-supervised methods. We continue these efforts on both fronts. First, we genera…
AgroGPT: Efficient Agricultural Vision-Language Model with Expert Tuning
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Georgia Institute of Technology（佐治亚理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
Michigan State University（密歇根州立大学）；Jeonbuk National University（国立全北大学）；North Carolina State University（北卡罗来纳州立大学）；Southeast University（东南大学）；Nanyang Technological University（南洋理工大学）
AgroBench: Vision-Language Model Benchmark in Agriculture
The University of Osaka（大阪大学）；Kyoto University（京都大学）；Tokyo Institute of Technology（东京工业大学）；National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所 (AIST)）；University of Oxford（牛津大学）；OMRON SINIC X（欧姆龙思尼克斯）
Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review
University of Salento（萨伦托大学）；National Research Council of Italy（意大利国家研究委员会）；University Paris-Est Creteil（巴黎东克雷泰伊大学）；Universite Polytechnique Hauts de France（上法兰西理工大学）；Universite de Lille（里尔大学）；CNRS（法国国家科学研究中心）
In agriculture, the majority of vision systems perform still image classification. Yet, recent work has highlighted the potential of spatial and temporal cues as a rich source of information to improve the classification performance. In this paper, we propose novel approaches to explicitly capture both spatial and temporal information to improve the classification of deep convolutional neural networks. We leverage available RGB-D images and robot odometry to perform inter-frame feature map spatial registration. This information is then fused within recurrent deep learnt models, to improve thei…
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
MBZ University of AI（人工智能大学）；CECS, Australian National University（计算机科学与工程系，澳大利亚国立大学）；Computer Vision Laboratory, Linköping University（链接öping大学计算机视觉实验室）
Artificial Intelligence is everywhere today. But unfortunately, Agriculture has not been able to get that much attention from Artificial Intelligence (AI). A lack of automation persists in the agriculture industry. For over many years, farmers and crop field owners have been facing a problem of trespassing of wild animals for which no feasible solution has been provided. Installing a fence or barrier like structure is neither feasible nor efficient due to the large areas covered by the fields. Also, if the landowner can afford to build a wall or barrier, government policies for building walls …
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…
Modular Transformer Architecture for Precision Agriculture Imaging
University Park The Pennsylvania State University PA, United States（帕克大学帕萨迪纳分校）；Engineering Division, Great Valley The Pennsylvania State University PA, United States（工程部，大山谷帕萨迪纳分校）
How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images? In this paper, we propose a novel, model-agnostic, data-fusion approach for vegetation-related computer vision tasks. Motivated by the various Vegetation Indices (VIs), which are introduced by domain experts, we systematically reviewed the VIs that are widely used in remote sensing and their feasibility to be incorporated in deep neural networks. To fully leverage the Near-Infrared channel, the traditional Red-Green-Blue channels, and Vegetation Index or its …
Institute for Food and Agricultural Research and Technology (IRTA)（食品与农业研究技术研究所（IRTA））
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall perfo…
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所（IAIS））；University of Bonn（波恩大学）；Institute for Sugar Beet Research (IfZ)（甜菜研究所（IfZ））；Technical University of Munich（慕尼黑工业大学）
UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated…
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…
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）
