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Site-Specific Based Models

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Decision Support Systems for Weed Management

Abstract

This chapter reviews the major conceptual approaches and specifications for the design of site-specific weed management decision support systems (SSWM-DSS), recent advances in the use of remote and ground platforms and sensors for information gathering and processing, and initial experiences translating this information into chemical and physical weed control actuations through decision algorithms and models.

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References

  • Andújar D, Escola A, Dorado J et al (2011) Weed discrimination using ultrasonic sensors. Weed Res 51:543–547

    Google Scholar 

  • Andújar D, Calle M, Fernández-Quintanilla C et al (2018) Three-dimensional modeling of weed plants using low-cost photogrammetry. Sensors 18:1077. https://doi.org/10.3390/s18041077

    Article  Google Scholar 

  • Arvidsson S, Perez-Rodriguez P, Mueller-Roeber B (2011) A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol 191:895–907

    PubMed  Google Scholar 

  • Barroso J, Fernàndez-Quintanilla C, Ruiz D et al (2004) Spatial stability of Avena sterilis ssp. ludoviciana populations under annual applications of low rates of imazamethabenz. Weed Res 44:178–186

    Google Scholar 

  • Berge T, Aastveit A, Fykse H (2008) Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals. Precis Agric 9:391–405

    Google Scholar 

  • Brown RB, Noble SD (2005) Site-specific weed management: sensing requirements—what do we need to see? Weed Sci 53:252–258

    CAS  Google Scholar 

  • Castaldi PF, Pascucci S et al (2017) Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precis Agric 18:76–94

    Google Scholar 

  • Castillejo-González IL, de Castro AI, Jurado-Expósito M et al (2019) Assessment of the persistence of Avena sterilis l. patches in wheat fields for site-specific sustainable management. Agronomy 9:30

    Google Scholar 

  • Christensen S, Heisel T, Walter AM et al (2003) A decision algorithm for patch spraying. Weed Res 43:276–284

    Google Scholar 

  • Christensen S, Sogaard HT, Kudsk P et al (2009) Site-specific weed control technologies. Weed Res 49:233–241

    Google Scholar 

  • de Castro AI, Jurado-Expósito M, Peña-Barragán JM et al (2012) Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precis Agric 13:302–321

    Google Scholar 

  • de Castro AI, López Granados F, Gómez-Candón D et al (2013) In-season site-specific control of cruciferous weeds at broad-scale using quickbird imagery. in: 9th European Conference on Precision Agriculture (9th ECPA). Lleida, Spain, Universidad de Lleida

    Google Scholar 

  • de Castro AI, Torres-Sánchez J, Peña JM et al (2018) An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens 10:285

    Google Scholar 

  • Dammer KH (2016) Real-time variable-rate herbicide application for weed control in carrots. Weed Res 56:237–246

    CAS  Google Scholar 

  • Dammer KH, Wartenberg G (2007) Sensor-based weed detection and application of variable herbicide rates in real time. Crop Prot 26:270–277

    Google Scholar 

  • Felton WL, Mccloy KR (1992) Spot spraying. Agric Eng 73:9–12

    Google Scholar 

  • Fennimore SA, Slaughter DC, Siemens MC et al (2016) Technology for automation of weed control in speciality crops. Weed Technol 30:823–837

    Google Scholar 

  • Fernandez-Quintanilla C, Dorado J, San Martin C et al (2011) A five-step approach for planning a robotic site-specific weed management program for winter wheat. In: Gonzalez-de-Santos P, Rabatel G (eds) RHEA-2011. Robotics and associated high-technologies and equipment for agriculture, Montpellier, pp 3–12

    Google Scholar 

  • Fernandez-Quintanilla C, Dorado J, Andujar D et al (2018) Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Res 58:259–272

    Google Scholar 

  • Franco C, Pedersen SM, Papaharalampos H et al (2017) The value of precision for image-based decision support in weed management. Precis Agric 18:366–382

    Google Scholar 

  • Gao J, Liao W, Nuyttens D et al (2018) Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery. Int J Appl Earth Obs Geoinform 67:43–53

    Google Scholar 

  • Gerhards R, Oebel H (2006) Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res 46:185–193

    Google Scholar 

  • Gómez-Candón D, López-Granados F, Caballero-Novella J et al (2012a) Sectioning remote imagery for characterization of Avena sterilis infestations. Part A: Weed abundance. Precis Agric 13:322–336

    Google Scholar 

  • Gómez-Candón D, López-Granados F, Caballero-Novella J et al (2012b) Sectioning remote imagery for characterization of Avena sterilis infestations. Part B: Efficiency and economics of control. Precis Agric 13:337–350

    Google Scholar 

  • Gonzalez-de-Santos P, Ribeiro A, Fernandez-Quintanilla C et al (2016) Fleets of robots for environmentally-safe pest control in agriculture. Precis Agric:1–41

    Google Scholar 

  • Gray CJ, Shaw DR, Gerard D et al (2008) Utility of multispectral imagery for soybean and weed species differentiation. Weed Technol 22:713–718

    Google Scholar 

  • Griffin TW (2016) Adoption of precision agricultural technology in Kansas. Kansas State University. Department of Agricultural Economics Extension. Publication. KFMA Research Article KSU-AgEcon-TG-2016. https://www.agmanager.info/adoption-precision-agricultural-technology-kansas. Accessed 21 Jan 2017

  • Guo Q, Wu F, Pang S et al (2017) Crop 3D-a LiDAR Based Platform For 3D High-Throughput Crop Phenotyping. Sci China Life Sci

    Google Scholar 

  • Gutiérrez PA, López-Granados F, Peña-Barragán JM et al (2008) Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data. Comput Elect Agric 60:122–132

    Google Scholar 

  • Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP (2016) Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions. Sensors 16:236

    Google Scholar 

  • Gutjar C, Gerhards R (2010) Decision rules for site-specific weed management. In: Oerke EC et al (eds) Precision crop protection—the challenge and use of heterogeneity. Springer Science+Business Media, Berlin, pp 223–239

    Google Scholar 

  • Hengl T (2006) Finding the right pixel size. Comput Geosci 32:1283–1298

    Google Scholar 

  • Huang H, Deng J, Lan Y et al (2018) Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors 18:3299. https://doi.org/10.3390/s18103299

    Article  CAS  Google Scholar 

  • Jiménez-Brenes FM, López-Granados F, Torres-Sánchez J et al (2019) Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management. PLoS One 14:e0218132. https://doi.org/10.1371/journal.pone.0218132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jurado-Expósito M, de Castro AI, Torres-Sánchez J, Jiménez-Brenes FM, López-Granados F (2019) Papaver rhoeas L. mapping with cokriging using UAV imagery. Precis Agric. https://doi.org/10.1007/s11119-019-09635-z

  • Lamastus-Stanford FE, Shaw DR (2004) Evaluation of site-specific weed management implementing the herbicide application decision support system (HADSS). Precis Agric 5:411–426

    Google Scholar 

  • Lambert JPT, Hicks HL, Childs DZ et al (2017) Evaluating the potential of unmanned aerial systems for mapping weeds at field scales: a case study with Alopecurus myosuroides. Weed Res 58(1):35–45. https://doi.org/10.1111/wre.12275

    Article  PubMed  PubMed Central  Google Scholar 

  • Liakos K, Busato P, Moshou D et al (2018) Machine learning in agriculture: a review. Sensors 18:2674

    Google Scholar 

  • López-Granados F, Peña-Barragan JM, Jurado-Exposito M et al (2008) Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res 48:28–37

    Google Scholar 

  • López-Granados F, Torres-Sánchez J, Serrano-Pérez A et al (2016) Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precis Agric 17:183–199

    Google Scholar 

  • Louargant M, Jones G, Faroux R (2018) Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sens 10:761. https://doi.org/10.3390/rs10050761

    Article  Google Scholar 

  • Ludovisi R, Tauro F, Salvati R et al (2017) UAV-based thermal imaging for high-throughput field phenotyping of black poplar response to drought. Front Plant Sci 8:1681. https://doi.org/10.3389/fpls.2017.01681

    Article  PubMed  PubMed Central  Google Scholar 

  • Lutman PJW, Miller PCH (2007) Spatially variable herbicide application technology; opportunities for herbicide minimisation and protection of beneficial weeds. Research Review No. 62, Home-Grown Cereals Authority (HGCA), UK

    Google Scholar 

  • Neeser C, Dille JA, Krishnan G et al (2004) WeedSOFT®: a weed management decision support system. Weed Sci 52:115–122

    CAS  Google Scholar 

  • Nguyen T, Slaughter D, Max N et al (2015) Structured Light-Based 3D Reconstruction System for Plants. Sensors (Basel, Switzerland). 15:18587–612. https://doi.org/10.3390/s150818587

  • Parsons DJ, Benjamin L, Clarke J et al (2009) Weed manager—a model-based decision support system for weed management in arable crops. Comput Elect Agric 65:155–167

    Google Scholar 

  • Peña JM, Torres-Sánchez J, de Castro AI et al (2013) Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One 8:e77151. https://doi.org/10.1371/journal.pone.0077151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peña-Barragán JM, López-Granados F, Jurado-Expósito M et al (2007) Mapping Ridolfia segetum patches in sunflower crop using remote sensing. Weed Res 47:164–172

    Google Scholar 

  • Peña-Barragán JM, López-Granados F, Jurado-Expósito M et al (2010) Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation. Precis Agric 11:568–585

    Google Scholar 

  • Pérez-Ortiz M, Peña JM, Gutiérrez PA et al (2015) A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Appl Soft Comput 37:533–544

    Google Scholar 

  • Pérez-Ortiz M, Peña JM, Gutiérrez PA et al (2016) Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Syst Appl 47:85–94

    Google Scholar 

  • Peteinatos G, Weis M, Andujar D et al (2014) Potential use of ground-based sensor technologies for weed detection. Pest Manag Sci 70:190–199

    CAS  PubMed  Google Scholar 

  • Pflanz M, Nordmeyer H, Schirrmann M (2018) Weed mapping with UAS imagery and a bag of visual words based image classifier. Remote Sens 10:1530. https://doi.org/10.3390/rs10101530

    Article  Google Scholar 

  • Rasmussen J, Nielsen J, Garcia-Ruiz F (2013) Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res 53:242–248

    Google Scholar 

  • Rydahl P, Berti A, Munier-Jolain N (2008) 24-Decision support systems (DSS) for weed control in Europe–state-of-the-art and identification of ‘best parts’ for unification on a European level. https://www.researchgate.net/publication/228431432_O_24. Accessed 7 Feb 2008

  • Sønderskov M, Rydahl P, Bøjer OM et al (2016) Crop protection online—weeds: a case study for agricultural decision support systems. In: Papathanasiou J, Ploskas N, Linden I (eds) Real-world decision support systems. Integrated Series in Information Systems, Springer International, Berlin, 37:303–320

    Google Scholar 

  • Torres-Sánchez J, López-Granados F, de Castro AI et al (2013) Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One 8:e58210. https://doi.org/10.1371/journal.pone.0058210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Weis M, Gerhards R (2007) Feature extraction for the identification of weed species in digital images for the purpose of site-specific weed control. In: Stafford JV (ed) Precision agriculture. Wageningen Academic, Wageningen, pp 537–545

    Google Scholar 

  • Yost MA, Sudduth KA, Walthall CL et al (2019) Public–private collaboration toward research, education and innovation opportunities in precision agriculture. Precis Agric 20:4–18

    Google Scholar 

  • Yu J, Sharpe SM, Schumann AW et al (2019) Deep learning for image-based weed detection in turfgrass. Eur J Agron 104:78–84

    Google Scholar 

  • Zhang Y, Slaughter DC (2011) Hyperspectral species mapping for automatic weed control in tomato under thermal environmental stress. Comput Elect Agric 77:95–104

    Google Scholar 

  • Zhang Y, Slaughter DC, Staab ES (2012a) Robust hyperspectral vision-based classification for multi-season weed mapping. J Photogram Remote Sens 69:65–73

    Google Scholar 

  • Zhang Y, Staab ES, Slaughter DC et al (2012b) Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Prot 41:96–105

    CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Consejo Superior de Investigaciones Cientificas (CSIC), Instituto de Ciencias Agrarias, Madrid, Spain

    Cesar Fernández-Quintanilla, José Dorado, Dionisio Andújar & J. M. Peña

Authors
  1. Cesar Fernández-Quintanilla
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  2. José Dorado
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  3. Dionisio Andújar
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  4. J. M. Peña
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Corresponding author

Correspondence to Cesar Fernández-Quintanilla .

Editor information

Editors and Affiliations

  1. Departamento de Agronomía, Universidad Nacional del Sur - CERZOS/CONICET, Bahía Blanca, Buenos Aires, Argentina

    Guillermo R. Chantre

  2. Department of Crop Protection, Institute for Sustainable Agriculture, Córdoba, Córdoba, Spain

    José L. González-Andújar

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Fernández-Quintanilla, C., Dorado, J., Andújar, D., Peña, J.M. (2020). Site-Specific Based Models. In: Chantre, G., González-Andújar, J. (eds) Decision Support Systems for Weed Management. Springer, Cham. https://doi.org/10.1007/978-3-030-44402-0_7

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