Site and time-specific early weed control is able to reduce herbicide use in maize - a case study


- Efficacy of UAVs and emergence predictive models for weed control has been confirmed.
- Combination of time-specific and site-specific weed control provides optimal results.
- Use of timely prescription maps can substantially reduce herbicide use.


Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.



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Arriaga FJ, Guzman J, Lowery B, 2017. Conventional Agricultural Production Systems and Soil Functions. In: Soil Health and Intensification of Agroecosytems. Elsevier, pp 109–25. Available from: DOI:

Astatkie T, Rifai MN, Havard P, Adsett J, Lacko-Bartosova M, Otepka P, 2007. Effectiveness of hot water, infrared and open flame thermal units for controlling weeds. Biol. Agric. Hortic. 25:1–12. DOI:

Auld BA, Tisdell CA, 1987. Economic thresholds and response to uncertainty in weed control. Agric. Syst. 25:219–27. DOI:

Ayerdi Gotor A, Marraccini E, Leclercq C, Scheurer O, 2020. Precision farming uses typology in arable crop-oriented farms in northern France. Precis. Agric. 21:131–46. Available from: DOI:

Baillie C, Fillols E, McCarthy C, Rees S, Staier T, 2013. Evaluating commercially available precision weed spraying technology for detecting weeds in sugarcane farming systems. Sugar Res. Aust. Ltd:1–88.

Bajwa AA, 2014. Sustainable weed management in conservation agriculture. Crop Prot. 65:105–13. Available from: DOI:

Ballesteros R, Ortega JF, Hernández D, Moreno MA, 2014. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain. Precis. Agric. 15:593–614. DOI:

Banca dati dei prodotti fitosanitari, 2020. Minist. Della Salut. Available from:

Bareth G, Aasen H, Bendig J, Gnyp ML, Bolten A, Jung A, Michels R, Soukkamäki J, 2015. Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements. Photogramm. - Fernerkundung - Geoinf. 2015:69–79. Available from: DOI:

Beasley VR, 2020. Direct and Indirect Effects of Environmental Contaminants on Amphibians, 2nd edn. Elsevier Inc. Available from: DOI:

Borra-Serrano I, Peña JM, Torres-Sánchez J, Mesas-Carrascosa FJ, López-Granados F, 2015. Spatial quality evaluation of resampled unmanned aerial vehicle-imagery for weed mapping. Sensors 15:19688–708. DOI:

Bradford KJ, 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248–60. DOI:[0248:AOHTTQ]2.0.CO;2

Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M, 2015. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7:4026–47. DOI:

Cerrudo D, Page ER, Tollenaar M, Stewart G, Swanton CJ, 2012. Mechanisms of Yield Loss in Maize Caused by Weed Competition. Weed Sci. 60:225–32. DOI:

Coble HD, Mortensen DA, 1992. The Threshold Concept and Its Application to Weed Science P. Weed Technol. 6:191–5. DOI:

Cohen J, 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20:37–46. Available from: DOI:

Colbach N, Chauvel B, Gauvrit C, Munier-Jolain NM, 2007. Construction and evaluation of ALOMYSYS modelling the effects of cropping systems on the blackgrass life-cycle: From seedling to seed production. Ecol. Modell. 201:283–300. DOI:

Congalton RG, 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:35–46. DOI:

Dorado J, Sousa E, Calha IM, González-Andújar JL, Fernández-Quintanilla C, 2009. Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Res. 49:251–60. DOI:

Elmolla ES, Chaudhuri M, Eltoukhy MM, 2010. The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. J. Hazard. Mater. 179:127–34. Available from: DOI:

European Food Safety Authority, 2018. The 2016 European Union report on pesticide residues in food. EFSA J. 16 DOI:

European Parliament, 2009. Regulation (EC) No 1107/2009. Off. J. Eur. Union 309:1–50. Available from:

FAO/WHO, 2016a. The international code of conduct on pesticide management : guidelines on highly hazardous pesticides. Available from:

FAO/WHO, 2016b. Manual on development and use of FAO and WHO specifications for pesticides.

FAO, 2006. World Reference Base for Soil Resources. World Soil Resources Report 103 . Rome.

FAO, 2014. Conservation agriculture. Conserv. Agric. Available from:

Foody GM, 2002. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80:185–201. Available from: DOI:

Foody G, 2008. Harshness in image classification accuracy assessment. Int. J. Remote Sens. 29:3137–58. DOI:

Forcella F, Benech Arnold RL, Sanchez R, Ghersa CM, 2000. Modeling seedling emergence. F. Crop. Res. 67:123–39. DOI:

Gaillard G, 2005. Life Cycle Assessment of Agricultural Production Systems: Current Issues and Future Perspectives. Int. Semin. Technol. Dev. Good Agric. Pract. Asia Ocean.:98–110.

Gerhards R, 2013. Site-Specific Weed Control. In: Heege H (ed) Precision in Crop Farming: Site Specific Concepts and Sensing Methods: Applications and Results. Springer Netherlands, Dordrecht, pp 273–94. DOI:

Ghoshen HZ, Holshouser DL, Chandler JM., 1996. Influence of Density on Johnsongrass (Sorghum halepense) Interference in Field Corn (Zea mays). Weed Res. 44:879–83. DOI:

Giacomo R, David G, 2017. Unmanned Aerial Systems (UAS) in Agriculture: Regulations and Good Practices.

Gimsing AL, Agert J, Baran N, Boivin A, Ferrari F, Gibson R, Hammond L, Hegler F, Jones RL, König W, Kreuger J, van der Linden T, Liss D, Loiseau L, Massey A, Miles B, Monrozies L, Newcombe A, Poot A, Reeves GL, Reichenberger S, Rosenbom AE, Staudenmaier H, Sur R, Schwen A, Stemmer M, Tüting W, Ulrich U, 2019. Conducting groundwater monitoring studies in Europe for pesticide active substances and their metabolites in the context of Regulation (EC) 1107/2009. DOI:

Gitelson AA, Stark R, Rundquist D, Gitelson AA, Kaufman YJ, Stark R, Rundquist D, 2002. Novel Algorithms for Remote Estimation of Vegetation Fraction. Remote Sens. Environ. 80:79–87. DOI:

Gitelson AA, Vina A, Arkebauer TJ, Rundquist DC, Keydan G, Leavitt B, 2003. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30:0–5. DOI:

Gonzalez-de-Soto M, Emmi L, Perez-Ruiz M, Aguera J, Gonzalez-de-Santos P, 2016. Autonomous systems for precise spraying – Evaluation of a robotised patch sprayer. Biosyst. Eng. 146:165–82. DOI:

Gopalapillai S, Tian L, Zheng J, 1999. Evaluation of a flow control system for site-specific herbicide applications. Trans. Am. Soc. Agric. Eng. 42:863–70. DOI:

Gupta PK, 2018. Toxicity of Herbicides. In: Ramesh GC (ed) Veterinary Toxicology: Basic and Clinical Principles: Third Edition, Third Edit. Elsevier Inc., pp 553–67. Available from: DOI:

Hall JC, Van Eerd LL, Miller SD, Micheal DK, Prather TS, Shaner DL, Singh M, Vaughn KC, Stephen C, Prather TS, Shaner DL, Singh M, Vaughn KC, 2000. Future Research Directions for Weed Science. Weed Technol. 14:647–58. DOI:[0647:FRDFWS]2.0.CO;2

Hamouz P, Hamouzová K, Holec J, Tyšer L, 2013. Impact of site-specific weed management on herbicide savings and winter wheat yield. Plant, Soil Environ. 59:101–7. DOI:

Hanzlik K, Gerowitt B, 2016. Methods to conduct and analyse weed surveys in arable farming: a review. Agron. Sustain. Dev. 36:1–18. Available from: DOI:

Hasenbein S, Peralta J, Lawler SP, Connon RE, 2017. Environmentally relevant concentrations of herbicides impact non-target species at multiple sublethal endpoints. Sci. Total Environ. DOI:

Hassanein M, El-Sheimy N, 2018. An efficient weed detection procedure using low-cost UAV imagery system for precision agriculture applications. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch. 42:181–7. DOI:

Heap I, LeBaron H, 2001. Introduction and Overview of Resistance. In: Powels SB, Shaner DL (eds) Herbicide resistance and world grains. CRC Press LLC, Boca Raton, pp 12–33. DOI:

Herwitz SR, Johnson LF, Dunagan SE, Higgins RG, Sullivan D V, Zheng J, Lobitz BM, Leung JG, Gallmeyer BA, Aoyagi M, Slye RE, Brass JA, 2004. Imaging from an unmanned aerial vehicle : agricultural surveillance and decision support. Comput. Electron. Agric. 44:49–61. DOI:

Holt JS, 2013. Herbicides. Encycl. Biodivers. Second Ed. 4:87–95. DOI:

Huang Y, Reddy KN, Fletcher RS, Pennington D, 2018. UAV Low-Altitude Remote Sensing for Precision Weed Management. Weed Technol. 32:2–6. DOI:

Hussain M, Farooq S, Merfield C, Jabran K, 2018. Mechanical weed control. In: Jabran K, Chauhan BS (eds) Non-Chemical Weed Control, 1st edn. Elsevier Inc., pp 133–55. Available from: DOI:

Idowu J, Angadi S, 2013. Understanding and Managing Soil Compaction in Agricultural Fields. Circ. 672:1–8. Available from:

Imoloame EO, Omolaiye JO, 2017. Weed Infestation, Growth and Yield of Maize (Zea mays L.) as Influenced by Periods of Weed Interference. Adv. Crop Sci. Technol. 05 DOI:

Irigaray C, Fernández T, El Hamdouni R, Chacón J, 2007. Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: Examples from the Betic Cordillera (southern Spain). Nat. Hazards 41:61–79. DOI:

Keller M, Gutjahr C, Möhring J, Weis M, Sökefeld M, Gerhards R, 2014. Estimating economic thresholds for site-specific weed control using manual weed counts and sensor technology: An example based on three winter wheat trials. Pest Manag. Sci. 70:200–11. DOI:

Kluza PA, Kuna-Broniowska I, Parafiniuk S, 2019. Modeling and prediction of the uniformity of spray liquid coverage from flat fan spray nozzles. Sustain. 11 DOI:

Koot TM, 2014. Weed detection with Unmanned Aerial Vehicles in agricultural systems. Wageningen University Available from:

Kudsk P, Streibig JC, 2003. Herbicides – a two-edged sword. Weed Res. 43:90–102. DOI:

Lambert JPT, Hicks HL, Childs DZ, Freckleton RP, 2018. Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides. Weed Res. 58:35–45. DOI:

Lingenfelter DD, Hartwig NL, 2013. Introduction to Weeds and Herbicides. Ag Commun. Mark. Pennsylvania State Univ.:1–38.

Lobley M, Potter C, 2004. Agricultural change and restructuring: Recent evidence from a survey of agricultural households in England. J. Rural Stud. 20:499–510. DOI:

López-Granados F, 2011. Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Res. 51:1–11. DOI:

López-Granados F, Torres-Sánchez J, Serrano-Pérez A, de Castro AI, Mesas-Carrascosa FJ, Peña JM, 2016. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precis. Agric. 17:183–99. DOI:

Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C, 2017. UAV-based crop and weed classification for smart farming. Proc. - IEEE Int. Conf. Robot. Autom.:3024–31. DOI:

Lyon DJ, Miller SD, Wicks GA, 1996. The future of herbicides in weed control systems of the Great Plains. J. Prod. Agric. 9:209–15. DOI:

Maes WH, Steppe K, 2019. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends Plant Sci. 24:152–64. Available from: DOI:

Martín CS, Andújar D, Fernández-Quintanilla C, Dorado J, 2015. Spatial Distribution Patterns of Weed Communities in Corn Fields of Central Spain. Weed Sci. 63:936–45. Available from: DOI:

Masin R, Cacciatori G, Zuin MC, Zanin G, 2010. AlertInf: Emergence predictive model for weed control in maize in Veneto. Ital. J. Agrometeorol.:5.

Masin R, Loddo D, Benvenuti S, Otto S, Zanin G, 2012. Modeling Weed Emergence in Italian Maize Fields. Weed Sci. 60:254–9. Available from: DOI:

Masin R, Loddo D, Gasparini V, Otto S, Zanin G, 2014. Evaluation of Weed Emergence Model AlertInf for Maize in Soybean. Weed Sci. 62:360–9. Available from: DOI:

McKinnon T, Hoff P, 2017. Comparing RGB-Based Vegetation Indices With NDVI For Drone Based Agricultural Sensing. Agribotix:1–8.

Melander B, Rasmussen IA, Bàrberi P, 2005. Integrating physical and cultural methods of weed control— examples from European research. Weed Sci. 53:369–81. DOI:

Mohler CL, 1996. Ecological bases for the cultural control of annual weeds. J. Prod. Agric. 9:468–74. DOI:

Morales MAM, Camargo B de CV, Hoshina MM, 2013. Toxicity of Herbicides: Impact on Aquatic and Soil Biota and Human Health. In: Price A, Kelton J (eds) Herbicides: Current Research and Case Studies in Use. IntechOpen Limited, London, UK, pp 399–443.

Mortensen DA, Johnson GA, Wyse DY, Martin AR, 1995. Managing Spatially Variable Weed Populations. In: Robert PC, Rust RH, Larson WE (eds) Site‐Specific Management for Agricultural Systems. American Society of Agronomy, pp 395–415. DOI:

Murat YS, Ceylan H, 2006. Use of artificial neural networks for transport energy demand modeling. Energy Policy 34:3165–72. DOI:

Murray AT, Shyy TK, 2000. Integrating attribute and space characteristics in choropleth display and spatial data mining. Int. J. Geogr. Inf. Sci. 14:649–67. DOI:

Myers MW, Curran WS, VanGessel MJ, Calvin DD, Mortensen D a., Majek B a., Karsten HD, Roth GW, 2004. Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52:913–9. DOI:

OPEN CV, 2014. Neural Networks. Available from:

Partel V, Charan Kakarla S, Ampatzidis Y, 2019. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput. Electron. Agric. 157:339–50. Available from: DOI:

Paul CJM, Nehring R, 2005. Product diversification, production systems, and economic performance in U.S. agricultural production. J. Econom. 126:525–48. DOI:

Pérez-Ortiz M, Peña JM, Gutiérrez PA, Torres-Sánchez J, Hervás-Martínez C, López-Granados F, 2015. A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Appl. Soft Comput. J. 37:533–44. DOI:

Pérez-Ortiz M, Peña JM, Gutiérrez PA, Torres-Sánchez J, Hervás-Martínez C, López-Granados F, 2016. Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Syst. Appl. 47:85–94. DOI:

Peruzzi A, Martelloni L, Frasconi C, Fontanelli M, Pirchio M, Raffaelli M, 2017. Machines for non-chemical intra-row weed control in narrow and wide-row crops: A review. J. Agric. Eng. 48:57–70. DOI:

Radosevich SR, Holt JS, Ghersa C, Radosevich SR, 2007. Ecology of weeds and invasive plants : relationship to agriculture and natural resource management. Wiley-Interscience. Available from: problem agriculture&f=false

Raghavan GSV, Alvo P, McKyes E, 1990. Soil Compaction in Agriculture: A View Toward Managing the Problem. In: Lal R, Stewart BA (eds) Advances in Soil Science, 11th edn. Springer, New York, pp 289–330. DOI:

Ricroch A, Chopra S, Fleischer SJ, 2014. Plant biotechnology: Experience and future prospects. Plant Biotechnol. Exp. Futur. Prospect.:1–291. DOI:

Roberts RK, Hayes RMR, 1989. Decision Criterion for Profitable Johnsongrass ( Sorghum halepense ) Management in Soybeans ( Glycine max ). Weed Technol. 3:44–7. DOI:

Rumelhart ED, Hinton EG, Williams JR, 1986. Learning representations by back-propagation errors. Nature 323:533–6. DOI:

Santín-Montanyá I, Francisco de Andrés E, Zambrana E, Tenorio JL, 2015. The Competitive Ability of Weed Community with Selected Crucifer Oilseed Crops. In: Price A, Kelton J, Sarunaite L (eds) Herbicides Agronomic Crops and Weed Biology. IntechOpen Limited, London, UK, pp 155–71. Available from: DOI:

Sartorato I, Berti A, Zanin G, 1996. Estimation of economic thresholds for weed control in soybean (Glycine max (L.) Merr.). Crop Prot. 15:63–8. DOI:

Schneider P, Roberts DA, Kyriakidis PC, 2008. A VARI-based relative greenness from MODIS data for computing the Fire Potential Index. Remote Sens. Environ. 112:1151–67. DOI:

Sherwani SI, Arif IA, Khan HA, 2015. Modes of Action of Different Classes of Herbicides. In: Price A, Kelton J, Sarunaite L (eds) Herbicides, Physiology of Action and Safety. IntechOpen Limited, London, UK, pp 165–86. DOI:


Soltani N, Dille AJ, Burke IC, Everman WJ, VanGessel MJ, Davis VM, Sikkema PH, 2016. Potential corn yield losses due to weeds in North America. Weed Technol. 30:979–84. DOI:

Sözen A, Arcaklioǧlu E, Özalp M, Kanit EG, 2004. Use of artificial neural networks for mapping of solar potential in Turkey. Appl. Energy 77:273–86. DOI:

Story M, Congalton RG, 1986. Remote Sensing Brief Accuracy Assessment: A User’s Perspective. Photogramm. Eng. Remote Sensing 52:397–9.

Stroppiana D, Villa P, Sona G, Ronchetti G, Candiani G, Pepe M, Busetto L, Migliazzi M, Boschetti M, 2018. Early season weed mapping in rice crops using multi-spectral UAV data. Int. J. Remote Sens. 39:5432–52. Available from: DOI:

Takács-György K, 2008. Economic aspects of chemical reduction in farming – future role of precision farming. Food Econ. - Acta Agric. Scand. Sect. C 5:114–22. DOI:

Thrall PH, Bever JD, Burdon JJ, 2010. Evolutionary change in agriculture: The past, present and future. Evol. Appl. 3:405–8. DOI:

Tiktak A, De Nie DS, Piñeros Garcet JD, Jones A, Vanclooster M, 2004. Assessment of the pesticide leaching risk at the Pan-European level. The EuroPEARL approach. J. Hydrol. 289:222–38. DOI:

Torres-Sánchez J, López-Granados F, De Castro AI, Peña-Barragán JM, 2013. Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PLoS One 8 DOI:

Turan NG, Mesci B, Ozgonenel O, 2011. The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice. Chem. Eng. J. 171:1091–7. Available from: DOI:

Vats S, 2015. Herbicides : History , Classifi cation and Genetic. In: Lichtfouse E (ed) Sustainable Agriculture Reviews. Springer International Publishing, pp 153–92. DOI:

Weis M, Gutjahr C, Ayala VR, Gerhards R, Ritter C, Schölderle F, 2008. Precision farming for weed management: Techniques. Gesunde Pflanz. 60:171–81. DOI:

Wolf T, 2009. Best Management Practices for Herbicide Application Technology. Prairie Soils Crop. J. 2:24–30. Available from:

Zanin G, Berti A, Sattin M, 1994. Estimation of economic thresholds for weed control in maize in Northern Italy. 5th EWRS Mediterr. Symp. ‘Weed Control Sustain. Agric. Mediterr. Area’:51–8.

Zarco-Tejada PJ, Hubbard N, Loudjani P, 2014. Precision Agriculture: an Opportunity for Eu Farmers- Potential Support With the Cap 2014 - 2020. Eur. Parliam. Dir. Intern. Policies:56. Available from:

Zimdahl LR, 2007. Fundamentals of weed science, 3rd edn. Elsevier.

Zimdahl LR, 2018. Introduction to Chemical Weed Control. In: Maragioglio N, Fernandez BJ (eds) Fundamentals of Weed Science, 5th edn. Academic Press, pp 391–416. DOI:

Special Issue on "Integrated Weed Management"
Precision weed control, remote sensing, predicting weed emergence, Sorghum halepense, maize.
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How to Cite
Nikolić, N., Rizzo, D., Marraccini, E., Ayerdi Gotor, A., Mattivi, P., Saulet, P., Persichetti, A., & Masin, R. (2021). Site and time-specific early weed control is able to reduce herbicide use in maize - a case study. Italian Journal of Agronomy, (AOP).