https://doi.org/10.4081/ija.2021.1780
Site- and time-specific early weed control is able to reduce herbicide use in maize - a case study
Submitted: 23 November 2020
Accepted: 19 February 2021
Published: 22 March 2021
Accepted: 19 February 2021
Abstract Views: 2877
PDF: 560
HTML: 53
HTML: 53
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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. This study combined site-specific and time-specific weed control methods to explore their collective benefits for precision weed control. Using the AlertInf model, a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for the 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 amount reduction ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and timespecific weed control might significantly reduce herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.
Highlights
- Efficacy of UAVs and emergence predictive models for weed control have been confirmed.
- Combination of time-specific and site-specific weed control provides optimal results.
- Use of timely prescription maps can substantially reduce herbicide use.
Arriaga FJ, Guzman J, Lowery B, 2017. Conventional agricultural production systems and soil functions. In: Soil Health and Intensification of Agroecosytems. Elsevier, Amsterdam, The Netherlands, pp 109-25.
DOI: https://doi.org/10.1016/B978-0-12-805317-1.00005-1
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: https://doi.org/10.1080/01448765.2007.10823205
Auld BA, Tisdell CA, 1987. Economic thresholds and response to uncertainty in weed control. Agric. Syst. 25:219-27.
DOI: https://doi.org/10.1016/0308-521X(87)90021-7
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.
DOI: https://doi.org/10.1007/s11119-019-09660-y
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: https://www.sciencedirect.com/science/article/pii/S0261219414002348?via%3Dihub
DOI: https://doi.org/10.1016/j.cropro.2014.07.014
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: https://doi.org/10.1007/s11119-014-9357-6
Banca dati dei prodotti fitosanitari, 2020. Ministero della Salute Available from: http://www.fitosanitari.salute.gov.it/fitosanitariwsWeb_new/FitosanitariServlet?ACTION=cercaSostanza&FROM=0&TO=49&PROVENIENZA=LISTA&SOST=1230
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.
DOI: https://doi.org/10.1127/pfg/2015/0256
Beasley VR, 2020. Direct and Indirect effects of environmental contaminants on amphibians, 2nd edn. Elsevier Inc., Amsterdam, The Netherlands.
DOI: https://doi.org/10.1016/B978-0-12-409548-9.11274-6
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: https://doi.org/10.3390/s150819688
Bradford KJ, 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248-60.
DOI: https://doi.org/10.1614/0043-1745(2002)050[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: https://doi.org/10.3390/rs70404026
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: https://doi.org/10.1614/WS-D-11-00127.1
Coble HD, Mortensen DA, 1992. The threshold concept and its application to weed science P. Weed Technol. 6:191-5.
DOI: https://doi.org/10.1017/S0890037X00034552
Cohen J, 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20:37-46.
DOI: https://doi.org/10.1177/001316446002000104
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: https://doi.org/10.1016/j.ecolmodel.2006.09.018
Congalton RG, 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:35-46.
DOI: https://doi.org/10.1016/0034-4257(91)90048-B
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: https://doi.org/10.1111/j.1365-3180.2008.00690.x
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.
DOI: https://doi.org/10.1016/j.jhazmat.2010.02.068
European Food Safety Authority, 2018. The 2016 European Union report on pesticide residues in food. EFSA J. 16.
DOI: https://doi.org/10.2903/j.efsa.2018.5348
European Parliament, 2009. Regulation (EC) No 1107/2009. Off. J. Eur. Union 309:1-50. Available from: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:309:0001:0050:en:PDF
FAO, 2006. World Reference Base for Soil Resources. World Soil Resources Report 103. Rome, Italy.
FAO, 2014. Conservation agriculture. Conserv. Agric. Available from: http://www.fao.org/conservation-agriculture/en/
FAO/WHO, 2016a. The international code of conduct on pesticide management: guidelines on highly hazardous pesticides. Available from: www.fao.org/publications
FAO/WHO, 2016b. Manual on development and use of FAO and WHO specifications for pesticides.
Foody GM, 2002. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80:185-201.
DOI: https://doi.org/10.1016/S0034-4257(01)00295-4
Foody G, 2008. Harshness in image classification accuracy assessment. Int. J. Remote Sens. 29:3137-58.
DOI: https://doi.org/10.1080/01431160701442120
Forcella F, Benech Arnold RL, Sanchez R, Ghersa CM, 2000. Modeling seedling emergence. F. Crop. Res. 67:123-39.
DOI: https://doi.org/10.1016/S0378-4290(00)00088-5
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: H. Heege (Ed.), Precision in crop farming: site specific concepts and sensing methods: applications and results. Springer Netherlands, Dordrecht, The Netherlands, pp. 273-94.
DOI: https://doi.org/10.1007/978-94-007-6760-7_10
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: https://doi.org/10.1017/S0043174500094868
Giacomo R, David G, 2017. Unmanned aerial systems (UAS) in agriculture: regulations and good practices. Available from: ???
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. Available from: ???
DOI: https://doi.org/10.1007/s00003-019-01211-x
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: https://doi.org/10.1016/S0034-4257(01)00289-9
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: https://doi.org/10.1029/2002GL016450
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: https://doi.org/10.1016/j.biosystemseng.2015.12.018
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: https://doi.org/10.13031/2013.13265
Gupta PK, 2018. Toxicity of herbicides. In: G.C. Ramesh (Ed.), Veterinary toxicology: basic and clinical principles: third edition, Third Edit. Elsevier Inc., Amsterdam, The Netherlands, pp 553-67.
DOI: https://doi.org/10.1016/B978-0-12-811410-0.00044-1
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: https://doi.org/10.1614/0890-037X(2000)014[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: https://doi.org/10.17221/599/2012-PSE
Hanzlik K, Gerowitt B, 2016. Methods to conduct and analyse weed surveys in arable farming: a review. Agron. Sustain. Dev. 36:1-18.
DOI: https://doi.org/10.1007/s13593-015-0345-7
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. 607-608:733-43.
DOI: https://doi.org/10.1016/j.scitotenv.2017.06.270
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: https://doi.org/10.5194/isprs-archives-XLII-1-181-2018
Heap I, LeBaron H, 2001. Introduction and overview of resistance. In: S.B. Powels, D.L. Shaner (Eds.), Herbicide resistance and world grains. CRC Press LLC, Boca Raton, FL, USA, pp. 12-33.
DOI: https://doi.org/10.1201/9781420039085.ch0
Herwitz SR, Johnson LF, Dunagan SE, Higgins RG, Sullivan DV, 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: https://doi.org/10.1016/j.compag.2004.02.006
Holt JS, 2013. Herbicides. Encycl. Biodivers. Second Ed. 4:87-95.
DOI: https://doi.org/10.1016/B978-0-12-384719-5.00070-8
Huang Y, Reddy KN, Fletcher RS, Pennington D, 2018. UAV low-altitude remote sensing for precision weed management. Weed Technol. 32:2-6.
DOI: https://doi.org/10.1017/wet.2017.89
Hussain M, Farooq S, Merfield C, Jabran K, 2018. Mechanical weed control. In: K. Jabran, B.S. Chauhan (Eds.), Non-chemical weed control, 1st edn. Elsevier Inc., Amsterdam, The Netherlands, pp. 133-55.
DOI: https://doi.org/10.1016/B978-0-12-809881-3.00008-5
Idowu J, Angadi S, 2013. Understanding and managing soil compaction in agricultural fields. Circ. 672:1-8.
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: https://doi.org/10.4172/2329-8863.1000267
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: https://doi.org/10.1007/s11069-006-9027-8
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: https://doi.org/10.1002/ps.3545
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: https://doi.org/10.3390/su11236716
Koot TM, 2014. Weed detection with unmanned aerial vehicles in agricultural systems. Wageningen University. Available from: http://edepot.wur.nl/333537
Kudsk P, Streibig JC, 2003. Herbicides - a two-edged sword. Weed Res. 43:90-102.
DOI: https://doi.org/10.1046/j.1365-3180.2003.00328.x
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: https://doi.org/10.1111/wre.12275
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: https://doi.org/10.1016/j.jrurstud.2004.07.001
López-Granados F, 2011. Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res. 51:1-11.
DOI: https://doi.org/10.1111/j.1365-3180.2010.00829.x
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: https://doi.org/10.1007/s11119-015-9415-8
Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C, 2017. UAV-based crop and weed classification for smart farming. pp 3024-31 in Proc. - IEEE Int. Conf. Robot. Autom.
DOI: https://doi.org/10.1109/ICRA.2017.7989347
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: https://doi.org/10.2134/jpa1996.0209
Maes WH, Steppe K, 2019. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 24:152-64.
DOI: https://doi.org/10.1016/j.tplants.2018.11.007
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.
DOI: https://doi.org/10.1614/WS-D-15-00031.1
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.
DOI: https://doi.org/10.1614/WS-D-11-00124.1
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.
DOI: https://doi.org/10.1614/WS-D-13-00112.1
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: https://doi.org/10.1614/WS-04-136R
Mohler CL, 1996. Ecological bases for the cultural control of annual weeds. J. Prod. Agric. 9:468-74.
DOI: https://doi.org/10.2134/jpa1996.0468
Morales MAM, Camargo B de CV, Hoshina MM, 2013. Toxicity of herbicides: impact on aquatic and soil biota and human health. In: A. Price, J. Kelton (Eds.), Herbicides: current research and case studies in use. IntechOpen Ltd., London, UK, pp 399-443.
Mortensen DA, Johnson GA, Wyse DY, Martin AR, 1995. Managing spatially variable weed populations. pp 395-415 in P.C. Robert, R.H. Rust, W.E. Larson (Eds.), Site‐specific management for agricultural systems. American Society of Agronomy.
DOI: https://doi.org/10.2134/1995.site-specificmanagement.c27
Murat YS, Ceylan H, 2006. Use of artificial neural networks for transport energy demand modeling. Energy Policy 34:3165-72.
DOI: https://doi.org/10.1016/j.enpol.2005.02.010
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: https://doi.org/10.1080/136588100424954
Myers MW, Curran WS, VanGessel MJ, Calvin DD, Mortensen DA, Majek BA, Karsten HD, Roth GW, 2004. Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52:913-9.
DOI: https://doi.org/10.1614/WS-04-025R
OpenCV, 2014. Neural networks. Available from: https://docs.opencv.org/2.4/modules/ml/doc/neural_networks.html
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.
DOI: https://doi.org/10.1016/j.compag.2018.12.048
Paul CJM, Nehring R, 2005. Product diversification, production systems, and economic performance in U.S. agricultural production. J. Econom. 126:525-48.
DOI: https://doi.org/10.1016/j.jeconom.2004.05.012
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: https://doi.org/10.1016/j.asoc.2015.08.027
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: https://doi.org/10.1016/j.eswa.2015.10.043
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: https://doi.org/10.4081/jae.2017.583
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: https://books.google.it/books?hl=it&lr=&id=2paeqsOV2I8C&oi=fnd&pg=PR5&dq=weeds+problem+agriculture&ots=mbgVprk7qL&sig=Wc8YCyhWpPL7DfOBvRX91ZHXkvg#v=onepage&q=weeds problem agriculture&f=false
Raghavan GSV, Alvo P, McKyes E, 1990. Soil compaction in agriculture: a view toward managing the problem. In: R. Lal, B.A. Stewart (Eds.), Advances in soil science, 11th edn. Springer, New York, NY, USA, pp 289-330.
DOI: https://doi.org/10.1007/978-1-4612-3322-0_1
Ricroch A, Chopra S, Fleischer SJ, 2014. Plant biotechnology: experience and future prospects. Plant Biotechnol. Exp. Futur. Prospect. 1-291.
DOI: https://doi.org/10.1007/978-3-319-06892-3
Roberts RK, Hayes RMR, 1989. Decision criterion for profitable Johnsongrass (Sorghum halepense) management in soybeans (Glycine max). Weed Technol. 3:44-7.
DOI: https://doi.org/10.1017/S0890037X00031298
Rumelhart ED, Hinton EG, Williams JR, 1986. Learning representations by back-propagation errors. Nature 323:533-6.
DOI: https://doi.org/10.1038/323533a0
Santín-Montanyá I, Francisco de Andrés E, Zambrana E, Tenorio JL, 2015. The competitive ability of weed community with selected crucifer oilseed crops. In: A. Price, J. Kelton, L. Sarunaite (Eds.), Herbicides agronomic crops and weed biology. IntechOpen Limited, London, UK, pp 155-71.
DOI: https://doi.org/10.5772/60849
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: https://doi.org/10.1016/0261-2194(95)00114-X
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: https://doi.org/10.1016/j.rse.2007.07.010
Sherwani SI, Arif IA, Khan HA, 2015. Modes of action of different classes of herbicides. In: A. Price, J. Kelton, L. Sarunaite (Eds.), Herbicides, physiology of action and safety. IntechOpen Ltd., London, UK, pp 165-86.
DOI: https://doi.org/10.5772/61779
Slaughter DC, Giles DK, Tauzer C, 1999. Precision offset spray system for roadway shoulder weed control. J. Transp. Eng. 125:364-71.
DOI: https://doi.org/10.1061/(ASCE)0733-947X(1999)125:4(364)
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: https://doi.org/10.1614/WT-D-16-00046.1
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: https://doi.org/10.1016/S0306-2619(03)00137-5
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.
DOI: https://doi.org/10.1080/01431161.2018.1441569
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: https://doi.org/10.1080/16507540903093242
Thrall PH, Bever JD, Burdon JJ, 2010. Evolutionary change in agriculture: the past, present and future. Evol. Appl. 3:405-8.
DOI: https://doi.org/10.1111/j.1752-4571.2010.00155.x
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: https://doi.org/10.1016/j.jhydrol.2003.11.030
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: https://doi.org/10.1371/journal.pone.0058210
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.
DOI: https://doi.org/10.1016/j.cej.2011.05.005
Vats S, 2015. Herbicides: History, classification and genetic. In: E. Lichtfouse (Ed.), Sustainable agriculture reviews. Springer International Publishing, Berlin, Germany, pp. 153-92.
DOI: https://doi.org/10.1007/978-3-319-09132-7_3
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: https://doi.org/10.1007/s10343-008-0195-1
Wolf T, 2009. Best management practices for herbicide application technology. Prairie Soils Crop. J. 2:24-30.
Zanin G, Berti A, Sattin M, 1994. Estimation of economic thresholds for weed control in maize in Northern Italy. 5th EWRS Mediterr. pp 51-8 in Symp. ‘Weed Control Sustain. Agric. Mediterr. Area’.
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: http://www.europarl.europa.eu/RegData/etudes/note/join/2014/529049/IPOL-AGRI_NT(2014)529049_EN.pdf
Zimdahl LR, 2007. Fundamentals of weed science, 3rd edn. Elsevier, Amsterdam, The Netherlands.
Zimdahl LR, 2018. Introduction to chemical weed control. In: N. Maragioglio, B.J. Fernandez (Eds.), Fundamentals of weed science, 5th edn. Academic Press, New York, NY, USA, pp 391-416.
DOI: https://doi.org/10.1016/B978-0-12-811143-7.00013-5
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, 16(4). https://doi.org/10.4081/ija.2021.1780
Copyright (c) 2021 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.