Torres-Sospedra and Nebot (2014)Torres-Sospedra J, Nebot P. Two-stage procedure based on smoothed ensembles of neural networks applied to weed detection in orange groves. Biosyst Eng. 2014;123:40-55. Available from: https://doi.org/10.1016/j.biosystemseng.2014.05.005 https://doi.org/10.1016/j.biosystemseng....
|
Two-stage procedure based on smoothed ensembles of neural networks applied to weed detection in orange groves |
2014 |
Pérez-Ortiz et al. (2016)Pérez-Ortiz M, Peña JM, Gutiérrez PA, Torres-Sánchez J, Hervás-Martínez C, López-Granados F. Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Syst Appl. 2016;47:85-94. Available from: https://doi.org/10.1016/j.eswa.2015.10.043 https://doi.org/10.1016/j.eswa.2015.10.0...
|
Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery |
2016 |
Schuster et al. (2016a)Schuster MZ, Pelissari A, Moraes A, Harrison SK, Sulc RM, Lustosa SBC et al. Grazing intensities affect weed seedling emergence and the seed bank in an integrated crop–livestock system. Agric Ecosyst Environ. 2016;232:232-9. Available from: https://doi.org/10.1016/j.agee.2016.08.005 https://doi.org/10.1016/j.agee.2016.08.0...
|
Grazing intensities affect weed seedling emergence and the seed bank in an integrated crop–livestock system |
2016 |
Schuster et al. (2016b)Schuster MZ, Martinichen D, Pelissari A, Lustosa SBC, Gazziero DLP. Floristic and phytosociology of weed in response to winter pasture sward height at Integrated Crop-Livestock in Southern Brazil. App Res Agrotechnol. 2016;9(2):19-26. Available from: https://doi.org/10.5935/10.5935/PAeT.V9.N2.02 https://doi.org/10.5935/10.5935/PAeT.V9....
|
Floristic and phytosociology of weed in response to winter pasture sward height at Integrated Crop-Livestock in Southern Brazil |
2016 |
Chavan and Nandedkar (2018)Chavan TR, Nandedkar AV. AgroAVNET for crops and weeds classification: a step forward in automatic farming. Comput Electron Agric. 2018;154:361-72. Available from: https://doi.org/10.1016/j.compag.2018.09.021 https://doi.org/10.1016/j.compag.2018.09...
|
AgroAVNET for crops and weeds classification: A step forward in automatic farmimg |
2018 |
Sabzi and Abbaspour-Gilandeh (2018)Sabzi S, Abbaspour-Gilandeh Y. Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and particle swarm algorithm. Measurement. 2018;126:22-36. Available from: https://doi.org/10.1016/j.measurement.2018.05.037 https://doi.org/10.1016/j.measurement.20...
|
Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and partincle swarm algorithm |
2018 |
Sandino and Gonzalez (2018)Sandino J, Gonzalez F. A novel approach for invasive weeds and vegetation surveys using UAS and artificial intelligence. Proceedings of the 23rd International Conference on Methods & Models in Automation & Robotics (MMAR); 2018; Miedzyzdroje, Poland. New York: Institute of Electrical and Electronics Engineers; 2018[access Mês dia, ano]. Available from: https://doi.org/10.1109/mmar.2018.8485874 https://doi.org/10.1109/mmar.2018.848587...
|
A Novel Approach for Invasive Weeds and Vegetation Surveys using UAS and Artificial Intelligence |
2018 |
Zhang et al. (2018)Zhang W, Hansen MF, Volonakis TN, Smith Melvyn, S Lyndon, Wilson J et al. Broad-leaf weed detection in pasture. Proceedings of the IEEE 3rd International Conference on Image, Vision and Computing (ICIVC); 2018; Chongqing, China. New York: Institute of Electrical and Electronics Engineers; 2018[access Mês dia, ano]. Available from: https://doi.org/10.1109/icivc.2018.8492831 https://doi.org/10.1109/icivc.2018.84928...
|
Broad-Leaf Weed Detection in Pasture |
2018 |
Abouzahir et al. (2018)Abouzahir S, Sadik M, Sabir E. Enhanced approach for weeds species detection using machine vision. Proceedings of the 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS); 2018; Kenitra, Morocco. New York: Institute of Electrical and Electronics Engineers; 2018[access Mês dia, ano]. Available from: https://doi.org/10.1109/icecocs.2018.8610505 https://doi.org/10.1109/icecocs.2018.861...
|
Enhanced Approach for Weeds Species Detection Using Machine Vision |
2018 |
Gao et al. (2018)Gao J, Nuyttens D, Lootens P, He Y, Pieters JG. Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosyst Eng. 2018;170:39-50. Available from: https://doi.org/10.1016/j.biosystemseng.2018.03.006 https://doi.org/10.1016/j.biosystemseng....
|
Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery |
2018 |
Yu et al. (2019)Yu J, Schumann AW, Cao Z, Sharpe SM, Boyd NS. Weed detection in perennial ryegrass with deep learning convolutional neural network. Front Plant Sci. 2019;10. Available from: https://doi.org/10.3389/fpls.2019.01422 https://doi.org/10.3389/fpls.2019.01422...
|
Weed Detection in Perennial Ryegrass With Deep Learning Convolutional Neural Network |
2019 |
Partel et al. (2019)Partel V, Kakarla SC, Ampatzidis Y. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput Electr Eng. 2019;157:339-50. Available from: https://doi.org/10.1016/j.compag.2018.12.048 https://doi.org/10.1016/j.compag.2018.12...
|
Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence |
2019 |
Sudars et al. (2020)Sudars K, Jasko J, Namatevs I, Ozola L, Badaukis N. Dataset of annotated food crops and weed images for robotic computer vision control. Data Br. 2020;31:105833. Available from: https://doi.org/10.1016/j.dib.2020.105833 https://doi.org/10.1016/j.dib.2020.10583...
|
Dataset of annotated food crops and weed images for robotic computer vision control |
2020 |
Qiao et al. (2020)Qiao X, Li YZ, Su GY, Tian HK, Zhang S, Sun ZY et al. MmNet: identifying mikania micrantha kunth in the wild via a deep convolutional neural network. J Integr Agric. 2020;19(5):1292-300. Available from: https://doi.org/10.1016/s2095-3119(19)62829-7 https://doi.org/10.1016/s2095-3119(19)62...
|
MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network |
2020 |
Souza et al. (2020)Souza MF, Amaral LR, Oliveira SRM, Coutinho MAN, Netto, Camila Ferreira. Spectral differentiation of sugarcane from weeds. Biosyst Eng. 2020;190:41-6. Available from: https://doi.org/10.1016/j.biosystemseng.2019.11.023 https://doi.org/10.1016/j.biosystemseng....
|
Spectral differentiation of sugarcane from weeds |
2020 |
Yan et al. (2020)Yan X, Deng X, Jin J. Classification of weed species in the paddy field with DCNN-learned features. Proceedings of the IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC); 2020; Chongqing, China. New York: Institute of Electrical and Electronics Engineers; 2020[access Mês dia, ano]. Available from: https://doi.org/10.1109/itoec49072.2020.9141894 https://doi.org/10.1109/itoec49072.2020....
|
Classification of weed species in the paddy field with DCNN-Learned features |
2020 |
Wang et al. (2020)Wang A, Xu Y, Wei X, Cui B. Semantic segmentation of crop and weed using an encoder-decoder network and image enhancement method under uncontrolled outdoor illumination. IEEE Access. 2020;8:81724-34. Available from: https://doi.org/10.1109/access.2020.2991354 https://doi.org/10.1109/access.2020.2991...
|
Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination |
2020 |
Yu et al. (2020)Yu J, Schumann AW, Sharpe SM, Li X, Boyd NS. Detection of grassy weeds in bermudagrass with deep convolutional neural networks. Weed Sci. 2020;68(5):545-52. Available from: https://doi.org/10.1017/wsc.2020.46 https://doi.org/10.1017/wsc.2020.46...
|
Detection of grassy weeds in bermudagrass with deep convolutional neural networks |
2020 |
Sabzi et al. (2020)Sabzi S, Abbaspour-Gilandeh Y, Arribas JI. An automatic visible-range video weed detection, segmentation and classification prototype in potato field. Heliyon. 2020;6(5):1-17. Available from: https://doi.org/10.1016/j.heliyon.2020.e03685 https://doi.org/10.1016/j.heliyon.2020.e...
|
An automatic visible-range video weed detection, segmentation and classification prototype in potato field |
2020 |
Hussain et al. (2021)Hussain N, Farooque AA, Schumann AW, Abbas F, Acharya B, Mckenzie-Gopsill A et al. Application of deep learning to detect Lamb's quarters (Chenopodium album L.) in potato fields of Atlantic Canada. Comput Electr Eng. 2021;182. Available from: http://dx.doi.org/10.1016/j.compag.2021.106040 http://dx.doi.org/10.1016/j.compag.2021....
|
Application of deep learning to detect Lamb's quarters (Chenopodium álbum L.) in potato fields of Atlantic Canada |
2021 |
Fawakherji et al. (2021)Fawakherji M, Potena C, Pretto A, Bloisi DD, Nsrdi D. Multi-spectral image synthesis for crop/weed segmentation in precision farming. Robot Auton Syst. 2021;146. Available from: https://doi.org/10.1016/j.robot.2021.103861 https://doi.org/10.1016/j.robot.2021.103...
|
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming |
2021 |
Siddiqui et al. (2021)Siddiqui SA, Fatima N, Ahmad A. Neural network based smart weed detection system. Proceedings of the International Conference on Communication, Control and Information Sciences (ICCISc); 2021; Kerala, India. New York: Institute of Electrical and Electronics Engineers; 2021[access Mês dia, ano]. Available from: http://dx.doi.org/10.1109/iccisc52257.2021.9484925 http://dx.doi.org/10.1109/iccisc52257.20...
|
Neural Network based Smart Weed Detection System |
2021 |
Monteiro et al. (2021)Monteiro AL, Souza MF, Lins HA, Teófilo TMS, Barros Júnior AP, Silva DV et al. A new alternative to determine weed control in agricultural systems based on artificial neural networks (ANNs). Field Crops Res. 2021;263. Available from: https://doi.org/10.1016/j.fcr.2021.108075 https://doi.org/10.1016/j.fcr.2021.10807...
|
A new alternative to determine weed control in agricultural systems based on artificial neural networks (ANNs) |
2021 |
Etienne et al. (2021)Etienne A, Ahmad A, Aggarwal V, Saraswat D. Deep learning-based object detection system for identifying weeds using UAS imagery. Remote Sens. 2021;13(24):1-22. Available from: https://doi.org/10.3390/rs13245182 https://doi.org/10.3390/rs13245182...
|
Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery |
2021 |
Shorewala et al. (2021)Shorewala S, Ashfaque A, Sidharth R, Verma U. Weed density and distribution estimation for precision agriculture using semi-supervised learning. IEEE Access. 2021;9:27971-86. Available from: https://doi.org/10.1109/access.2021.3057912 https://doi.org/10.1109/access.2021.3057...
|
Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning |
2021 |
Subeesh et al. (2022)Subeesh A, Bhole S, Singh K, Chandel NS, Rajwade YA, Rao KVR, Kumar SP et al. Deep convolutional neural network models for weed detection in polyhouse grown bell peppers. Artif Intell Agric. 2022;6:47-54. Available from: https://doi.org/10.1016/j.aiia.2022.01.002 https://doi.org/10.1016/j.aiia.2022.01.0...
|
Deep convolutional neural network models for weed detection in polyhouse grown bell peppers |
2022 |
Nasiri et al. (2022)Nasiri A, Omid M, Taheri-Garavand A, Jafari A. Deep learning-based precision agriculture through weed recognition in sugar beet fields. Sustain Comput Informatics Systems. 2022;35. Available from: https://doi.org/10.1016/j.suscom.2022.100759 https://doi.org/10.1016/j.suscom.2022.10...
|
Deep learning-based precision agriculture through weed recognition in sugar beet fields |
2022 |
Alrowais et al. (2022)Alrowais F, Asiri MM, Alabdan R, Marzouk R, Hilal AM, Alkhayyat A et al. Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment. Comput Electr Eng. 2022;104(4). Available from: https://doi.org/10.1016/j.compeleceng.2022.108411 https://doi.org/10.1016/j.compeleceng.20...
|
Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment |
2022 |
Razfar et al. (2022)Razfar N, True J, Bassiouny R, Venkatesh VI, Kashef R. Weed detection in soybean crops using custom lightweight deep learning models. J Agric Food Res. 2022;8. Available from: https://doi.org/10.1016/j.jafr.2022.100308 https://doi.org/10.1016/j.jafr.2022.1003...
|
Weed detection in soybean crops using custom lightweight deep learning models |
2022 |
Costello et al. (2022)Costello B, Osunkoya OO, Sandino J, Marinic W, Trotter P, Shi B et al. Detection of parthenium weed (Parthenium hysterophorus L.) and Its growth stages using artificial intelligence. Agriculture. 2022;12(11):1-23. Available from: https://doi.org/10.3390/agriculture12111838 https://doi.org/10.3390/agriculture12111...
|
Detection of Parthenium Weed (Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence |
2022 |
Dominschek et al. (2022)Dominschek R, Schuster MZ, Barroso AAM, Moraes A, Anghinoni I, Carvalho PCF. Diversification of traditional paddy field impacts target species in weed seedbank. Rev Cienc Agron. 2022;53:1-10. Available from: https://doi.org/10.5935/1806-6690.20220030 https://doi.org/10.5935/1806-6690.202200...
|
Diversification of traditional paddy field impacts target species in weed seedbank |
2022 |
Ni et al. (2022)Ni C, Tian B, Wang X, Sun Y, Fei C. A deep convolutional neural network-based method for identifying weed seedlings in maize fields. Proceedings of 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC); 2022; Chongqing, China. New York: Institute of Electrical and Electronics Engineers; 2022[access Mês dia, ano]. Available from: https://doi.org/10.1109/imcec55388.2022.10019943 https://doi.org/10.1109/imcec55388.2022....
|
A deep convolutional neural network-based method for identifying weed seedlings in maize fields |
2022 |
Ngo et al. (2022)Ngo K, Chua J, Chun B, Ai RT. Automated weed detection system for bokchoy using computer vision. Proceedings of 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM); 2022; Boracay Island, Philippines. New York: Institute of Electrical and Electronics Engineers; 2022[access Mês dia, ano]. Available from: https://doi.org/10.1109/hnicem57413.2022.10109618 https://doi.org/10.1109/hnicem57413.2022...
|
Automated Weed Detection System for Bokchoy Using Computer Vision |
2022 |
Jose et al. (2022)Jose JA, Sharma A, Sebastian M, Densil RVF. Classification of weeds and crops using transfer learning. Proceedings of the 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI); 2022; Chennai, India. New York: Institute of Electrical and Electronics Engineers; 2022[access Mês dia, ano]. Available from: http://dx.doi.org/10.1109/accai53970.2022.9752477 http://dx.doi.org/10.1109/accai53970.202...
|
Classification of Weeds and Crops using Transfer Learning |
2022 |
Wang and Leelapatra (2022)Wang MY, Leelapatra W. Weeding robot based on lightweight platform and dual cameras. Skima. 2022;31:1-7. Available from: https://doi.org/10.1109/skima57145.2022.10029527 https://doi.org/10.1109/skima57145.2022....
|
Weeding Robot Based on Lightweight Platform and Dual Cameras |
2022 |
Firmansyah et al. (2022)Firmansyah E, Suparyanto T, Hidayat AA, Pardamean B. Real-time weed identification using machine learning and image processing in oil palm plantations. IOP Conf Ser Earth Environ Sci. 2022;998:1-8. Available from: https://doi.org/10.1088/1755-1315/998/1/012046 https://doi.org/10.1088/1755-1315/998/1/...
|
Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations |
2022 |
Meena et al. (2023)Meena SD, Susank M, Guttula T, Chandana SH, Sheela J. Crop Yield Improvement with weeds, pest and disease detection. Procedia Comput Sci. 2023;218:2369-82. Available from: https://doi.org/10.1016/j.procs.2023.01.212 https://doi.org/10.1016/j.procs.2023.01....
|
Crop Yield Improvement with Weeds, Pest and Disease Detection |
2023 |
Ajayi and Ashi (2023)Ajayi OG, Ashi J. Effect of varying training epochs of a faster region-based convolutional neural network on the accuracy of an automatic weed classification scheme. Smart Agric Technol. 2023;3:100-28. Available from: https://doi.org/10.1016/j.atech.2022.100128 https://doi.org/10.1016/j.atech.2022.100...
|
Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme |
2023 |
Dang et al. (2023)Dang F, Chen D, Lu Y, Li Z. YOLOWeeds: a novel benchmark of yolo object detectors for multi-class weed detection in cotton production systems. Comput Electron Agric. 2023;205. Available from: https://doi.org/10.1016/j.compag.2023.107655 https://doi.org/10.1016/j.compag.2023.10...
|
YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems |
2023 |
Raja et al. (2023)Raja R, Slaughter DC, Fennimore SA, Siemens MC. Real-time control of high-resolution micro-jet sprayer integrated with machine vision for precision weed control. Biosyst Eng. 2023;228:31-48. Available from: https://doi.org/10.1016/j.biosystemseng.2023.02.006 https://doi.org/10.1016/j.biosystemseng....
|
Real-time control of high-resolution micro-jet sprayer integrated with machine vision for precision weed control |
2023 |