Machine learning algorithms applied to weed management in integrated crop-livestock systems: a systematic literature review

Gomes, Ana L. B.; Fernandes, Anita M. R.; Horta, Bruno A. C.; Oliveira, Maurílio F. de

doi:10.51694/AdvWeedSci/2024;42:00004

Ana L. B. Gomes ^**Corresponding author: <gomes.ana@edu.univali.br>

read and agreed to the published version of the manuscript

Conceptualization of the manuscript and development of the methodology

data collection and curation

data analysis

data interpretation

writing the original draft of the manuscript

University of Vale do Itajaí, Itajaí, SC, Brazil.

https://orcid.org/0009-0001-9996-1455

Anita M. R. Fernandes

read and agreed to the published version of the manuscript

Conceptualization of the manuscript and development of the methodology

funding acquisition and resources

project administration

writing, review, and editing

University of Vale do Itajaí, Itajaí, SC, Brazil.

https://orcid.org/0000-0002-2986-5353

Bruno A. C. Horta

read and agreed to the published version of the manuscript

supervision

writing, review, and editing

University of Vale do Itajaí, Itajaí, SC, Brazil.

https://orcid.org/0000-0002-3952-1474

Maurílio F. de Oliveira

read and agreed to the published version of the manuscript

funding acquisition and resources

supervision

writing, review, and editing

Embrapa Maize and Sorghum, Sete Lagoas, MG, Brazil.

https://orcid.org/0000-0001-5527-2079

*Corresponding author: <gomes.ana@edu.univali.br>

Approved by:

Editor in Chief: Carlos Eduardo Schaedler

Associate Editor: Aldo Merotto Junior

Conflict of Interest: The authors declare that there is no conflict of interest regarding the publication of this manuscript.

Data base	Search String	URL
IEEE Xplore	[TITLE-ABS-KEY(weed(s)) OR TITLE-ABS-KEY(weed control) OR TITLE-ABS-KEY(weed management) AND TITLE-ABS-KEY(machine learning) OR TITLE-ABS-KEY(artificial intelligence)] OR [TITLE-ABS-KEY(weed(s) OR TITLE-ABS-KEY(weed control) OR TITLE-ABS-KEY(weed management) AND TITLE-ABS-KEY(ICLS) OR TITLE-ABS-KEY(ICL system))]	https://ieeexplore.ieee.org/Xplore/home.jsp/ (accessed on 26 May 2023)
Mendeley	[("weed(s)" OR "weed control" OR "weed management") AND ("machine learning" OR "artificial intelligence")] OR [("weed(s)" OR "weed control" OR "weed management") AND ("ICLS" OR "ICL system")]	https://www.mendeley.com/search/ (accessed on 23 May 2023)
Science Direct	[("weed(s)" OR "weed control" OR "weed management") AND ("machine learning" OR "artificial intelligence")] OR [("weed(s)" OR "weed control" OR "weed management") AND ("ICLS" OR "ICL system")]	https://www.sciencedirect.com/ (accessed on 26 May 2023)
Scopus	[(("All Metadata": "weed(s)") OR ("All Metadata": "weed control") OR ("All Metadata": "weed management")) AND (("All Metadata": "machine learning") OR ("All Metadata": "artificial intelligence"))] OR [(("All Metadata": "weed(s)") OR ("All Metadata": "weed control") OR ("All Metadata": "weed management")) AND (("All Metadata": "ICLS") OR ("All Metadata": "ICL system"))]	https://www.scopus.com/home.uri/ (accessed on 25 May 2023)

Inclusion Criteria	Exclusion Criteria
IC1: Articles published in english.	EC1: Duplicated articles.
IC2: Articles published between January, 2013 and March, 2023.	EC2: Articles that do not allow access to their full content.
	EC3: Articles that do not have "weed management", "weed control" or "weed(s)" in the title, abstract or keywords.

Database	Studies found	Stage 1	Stage 2	Stage 3
IEEE Xplore	14	12	7	7
Mendeley	20	13	4	4
Science Direct	303	276	17	15
Scopus	159	148	35	14
Total	496	449	63	40

Reference	Title	Year
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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
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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

Study	Crops	Machine Learning algorithms	Developed solutions
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....	Orange	SVM, ANN and MLP	Not available
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...	Maize and Sunflower	MV	Not available
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...	Pasture	Not available	Not available
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....	Types of grass	Not available	Not available
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...	Maize, Wheat, Sugar cane and Types of grass	CNN	Not available
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...	Potato	ANN and MV	Smart sprayers
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...	Types of grass	MV	Framework
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...	Pasture	DL	Not available
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...	Soy	SVM, MV and BPNN	Not available
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....	Maize	KNN	Hyperspectral cameras
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...	Types of grass	MV and DCNN	Smart sprayers
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...	Pepper	ANN and MV	Smart sprayers
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[1] *Corresponding author: <gomes.ana@edu.univali.br>

Brasil

Brasil

Machine learning algorithms applied to weed management in integrated crop-livestock systems: a systematic literature review

Abstract: