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Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture [article]

Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti, Christopher J. Henry, Cara M. Godee, Manisha Ajmani
2021 arXiv   pre-print
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.  ...  Conclusion and data availability In this paper we presented an extensive dataset of labelled plant images. These images show crops and weeds as common in the Canadian prairies and northern US states.  ... 
arXiv:2108.05789v1 fatcat:r2tiibwjnrf5dpyu2cg3jwyu7m

A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks [chapter]

Sebastian Haug, Jörn Ostermann
2015 Lecture Notes in Computer Science  
In this paper we propose a benchmark dataset for crop / weed discrimination, single plant phenotyping and other open computer vision tasks in precision agriculture.  ...  Intra-and inter-row weeds were present, weed and crop were approximately of the same size and grew close together.  ...  Acknowledgments The authors thank the following colleagues for their comments and help with the acquisition of the dataset: Wolfram Strothmann, Fabian Sellmann, Arno Ruckelshausen, Susanne Fittje, Frederik  ... 
doi:10.1007/978-3-319-16220-1_8 fatcat:xno7a6ffmjhk7e4djflrqwiajm

Review of Weed Detection Methods Based on Computer Vision

Zhangnan Wu, Yajun Chen, Bo Zhao, Xiaobing Kang, Yuanyuan Ding
2021 Sensors  
It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets,  ...  However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose.  ...  [36] Support vector machine with adaptive boosting Precision:95.85% Acknowledgments: The authors thank the editors and anonymous reviewers for providing helpful suggestions to improve the quality of  ... 
doi:10.3390/s21113647 pmid:34073867 fatcat:dyaxmopki5aqhji7u2dv465a44

Building an Aerial-Ground Robotics System for Precision Farming [article]

Alberto Pretto, Stéphanie Aravecchia, Wolfram Burgard, Nived Chebrolu, Christian Dornhege, Tillmann Falck, Freya Fleckenstein, Alessandra Fontenla, Marco Imperoli, Raghav Khanna, Frank Liebisch, Philipp Lottes (+16 others)
2020 arXiv   pre-print
This paper presents an overview of the scientific and technological advances and outcomes obtained in the project.  ...  We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops.  ...  III), such as inference of weed density from multi-spectral images, mapping and classification of crops and weeds, and computation of plant health indicators.  ... 
arXiv:1911.03098v2 fatcat:gcmmlqqzuvg4flxforwbinrtkq

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Bini Darwin, Pamela Dharmaraj, Shajin Prince, Daniela Elena Popescu, Duraisamy Jude Hemanth
2021 Agronomy  
The automation in image analysis with computer vision and deep learning models provides precise field and yield maps.  ...  Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting.  ...  Recent advances in computer vision enable us to analyze each pixel of an image.  ... 
doi:10.3390/agronomy11040646 fatcat:n3ru7ggspvgixlcu24meshbax4

The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review

Nursyazyla Sulaiman, Nik Norasma Che'Ya, Muhammad Huzaifah Mohd Roslim, Abdul Shukor Juraimi, Nisfariza Mohd Noor, Wan Fazilah Fazlil Ilahi
2022 Applied Sciences  
Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed  ...  Plants must be divided into crops and weeds based on their species for successful weed detection.  ...  The dataset included a small database of corn seedlings and weed and actual field images.  ... 
doi:10.3390/app12052570 fatcat:cfifs74ybvdhnc6y6wai7brq3a

FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications

Shoaib Kamal, Vaishali Gajendra Shende, Korla Swaroopa, P. Bindhu Madhavi, Patan Saleem Akram, Kumud Pant, Shantala Devi Patil, Kibebe Sahile, Deepak Kumar Jain
2022 Wireless Communications and Mobile Computing  
An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures.  ...  The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden.  ...  Article [15] provides a computer vision system capable of distinguishing between weeds and crop plants under unrestricted light in real time.  ... 
doi:10.1155/2022/2770706 fatcat:7dvf6lcn7bbnrj4atf6wfaaz5a

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning

Alex Olsen, Dmitry A. Konovalov, Bronson Philippa, Peter Ridd, Jake C. Wood, Jamie Johns, Wesley Banks, Benjamin Girgenti, Owen Kenny, James Whinney, Brendan Calvert, Mostafa Rahimi Azghadi (+1 others)
2019 Scientific Reports  
Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture.  ...  These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.  ...  Acknowledgments This work is funded by the Australian Government Department of Agriculture and Water Resources Control Tools and Technologies for Established Pest Animals and Weeds Programme (Grant No.  ... 
doi:10.1038/s41598-018-38343-3 pmid:30765729 pmcid:PMC6375952 fatcat:x5bivzp7qrby3oszn4f367apoe

Bionic Technology and Deep Learning in Agricultural Engineering: Current Status and Future Prospects

Chunlei Tu, Jinxia Li, Xingsong Wang, Shen Cheng, Jie Li
2021 American Journal of Biochemistry and Biotechnology  
In recent years, as an extension of bionic technology, machine vision and deep learning have been widely used in agricultural production.  ...  As one of the most important production activity of mankind, agriculture plays an important role in social development.  ...  Acknowledgement This work was supported by the Key Technology Project of prevention and control of major accidents in production safety [grant numbers: jiangsu-0002-2017AQ].  ... 
doi:10.3844/ajbbsp.2021.217.231 fatcat:ujax3i2h6ze4ldi5jg7nsl324m

An Autonomous Robotic System for Mapping Weeds in Fields

Karl D. Hansen, Francisco Garcia-Ruiz, Wajahat Kazmi, Morten Bisgaard, Anders la Cour-Harbo, Jesper Rasmussen, Hans Jørgen Andersen
2013 IFAC Proceedings Volumes  
In ASETA, unmanned aircraft and unmanned ground vehicles are used to automate the task of identifying and removing weeds in sugar beet fields.  ...  The framework for a working automatic robotic weeding system is presented along with the implemented computer vision systems.  ...  Cooperation In terms of solving the basic tasks for a crop and weed management system, the automatic planning is capable of producing the necessary waypoints for this task.  ... 
doi:10.3182/20130626-3-au-2035.00055 fatcat:pmnjqz2qnfdpvkn73c5cwlitsq

A modern deep learning framework in robot vision for automated bean leaves diseases detection

Sudad H Abed, Alaa S Al-Waisy, Hussam J Mohammed, Shumoos Al-Fahdawi
2021 International Journal of Intelligent Robotics and Applications  
The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust).  ...  To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed.  ...  In most cases, image processing and computer vision techniques play an essential role in increasing the productivity of the crops by identifying and treating the disease in the early stages.  ... 
doi:10.1007/s41315-021-00174-3 pmid:33948485 pmcid:PMC8085806 fatcat:emqak3miqzflplnwi6h56ulzoe

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti, Christopher J. Henry, Cara M. Godee, Manisha Ajmani, Jeonghwan Gwak
2020 PLoS ONE  
We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.  ...  To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture.  ...  Ibrahim GPU Educational Lab at the University of Winnipeg, which we used extensively for the computing resources needed here; Rafael Otfinowski, Karina Kachur and Tabitha Wood for providing us with seeds  ... 
doi:10.1371/journal.pone.0243923 pmid:33332382 fatcat:usndjk5lozdbhmeplz2ktzuqva

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

Muhammet Fatih Aslan, Akif Durdu, Kadir Sabanci, Ewa Ropelewska, Seyfettin Sinan Gültekin
2022 Applied Sciences  
This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV  ...  In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors.  ...  Acknowledgments: The authors are grateful to the RAC-LAB (www.rac-lab.com (Accessed date: 6 December 2021)) for training and support.  ... 
doi:10.3390/app12031047 fatcat:2pwrdbkobff6vesy3bmlxxj5n4

Opportunities for Robotic Systems and Automation in Cotton Production

Edward Barnes, Gaylon Morgan, Kater Hake, Jon Devine, Ryan Kurtz, Gregory Ibendahl, Ajay Sharda, Glen Rains, John Snider, Joe Mari Maja, J. Alex Thomasson, Yuzhen Lu (+10 others)
2021 AgriEngineering  
Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic  ...  Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting.  ...  [74] used a stereoscopic camera, machine vision processing, a deep learning network model (YOLOv3), and an embedded computer to manage computation of the images to identify cotton bolls in the field  ... 
doi:10.3390/agriengineering3020023 fatcat:pvcifsr5c5hzthehqnts6fda6y

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Samuel C. Hassler, Fulya Baysal-Gurel
2019 Agronomy  
This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting  ...  This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.  ...  processing times can be very extensive and require a great deal of computing time when using high-resolution images.  ... 
doi:10.3390/agronomy9100618 fatcat:26n3blorinfarga5chpymlidxu
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