Simulation modelling of mechanical systems for intra-row weeding in a precision farming approach

Keywords: designing, organic farming, lighter working tools, precision control weeding


Aim of study: To test new approaches to perform mechanical weeding inside the row in horticulture and tree fruit fields. The idea is to weed the row by skipping the crop by means of a rotating system instead of a traditional crosswise one.

Area of study: North of Italy.

Material and methods: Numerical models have been developed to simulate mechanical weeding over time by generating numerical maps to quantify the different kind of worked areas.

Main results: Considering the efficiency of weed control on the row, the rotating plant-skipping system with vertical axis (RPSS-VA model) with two working tools gives the best performance index (1.1.RWA% = 95.9%). A similar performance can be obtained by the crosswise displacement plant-skipping system, but with very high crosswise translation velocity (with va/vr ratio = 1/5, 1.1.RWA% = 94.5%). With regard to the outwards worked area the RPSS-VA models give the best performances (2.2.%OWAR index from 127.2% up to 282.3%). To reduce the worked area outside the row, the FBTS models give lower index (2.1.OWAR%), while the RPSS-HA works only on the row, but with the lower 1.1.RWA% index among all tested models (55.8%).

Research highlights: Rotating systems resulted more efficient than traditional ones, and provide considerations on the use of electric drive power instead of hydraulic one. This study highlights also the need of new approaches in designing lighter working tools. Lastly, the proposed classification of the worked areas could be used as reference standard.


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Assirelli A, Liberati P, Santangelo E, Del Giudice A, Civitarese V, Pari L, 2015. Evaluation of sensors for poplar cutting detection to be used in intra-row weed control machine. Comput Electron Agr 115: 161-170.

Assirelli A, Santangelo E, Spinelli R, Pari L, 2016. A single-pass to reduce tillage technique for the establishment of short-rotation poplar (Populus spp) plantation. Croat J For Eng 37 (1): 61-69.

Davies DHK, Hoad S, Maskell PR, Topp K, 2004. Looking at cereal varieties to help reduce weed control inputs. Proc Crop Protection in Northern Britain. Scott Agr Coll, Bush Estate, Penicuik, Midlothian, UK.

Fennimore SA, Slaughter DC, Siemens MC, Ramon GL, Mazin NS, 2016. Technology for automation of weed control in specialty crops. Weed Technol 30: 823-837.

Gaines TA, Zhang W, Wang D, Bukuna B, Chisholm ST, Shaner DL, et al., 2010. Gene amplification confers glyphosate resistance in Amaranthus palmeri. Proc Natl Acad Sci 107: 1029-1034.

Granatstein D, 2018. Weed management in orchards. WSU Extension, Wenatchee, WA, USA.

Hoad S, Topp C, Davies K, 2008. Selection of cereals for weed suppression in organic agriculture: a method based on cultivar sensitivity to weed growth. Euphytica 163: 355-366.

Home M, 2003. An investigation into the design of cultivation systems for inter- and intra-row weed control. PhD Thesis, Cranfield University, Silsoe, UK.

Kumar SP, TewariVK, Abhilash KC, Mehta CR, Brajesh N, Chethan CR, et al., 2020. A fuzzy logic algorithm derived mechatronic concept prototype for crop damage avoidance during eco-friendly eradication of intra-row weeds. Artific Intellig Agr 4: 116-126.

Kunz C, Weber JF, Peteinatos GG, Sökefeld M, Gerhards R, 2018. Camera steered mechanical weed control in sugar beet maize and soybean. Precis Agric 19: 708-720.

Ishida Y, Okamoto T, Imouk K, Kaizu Y, 2005. A study on physical weeding using a water jet. J Jap Soc Agric Machin 67(2): 93-99.

ISTAT, 2013. 6° Censimento Generale Agricoltura (General Census of agriculture). [Aug 2020].

Jabran K, Chauhan BS (eds.), 2018. Overview and significance of non-chemical weed control. In: Non chemical weed control, 1st ed., pp: 1-8. Elsevier, NY, USA.

Kurstjens DAG, Kropff MJ, 2001. The impact of uprooting and soil-covering on the effectiveness of weed harrowing. Weed Res 41: 211-228.

Jebu MD, Massetani F, Murri G, Neri D, 2020a. Sustainable alternatives to chemicals for weed control in the orchard - A review. Hortic Sci 47(1)

Jebu MD, Massetani F, Murri G, Facchi J, Monaci E, Amadio L, Neri D, 2020b. Integrated weed management in high density fruit orchards. Agronomy 10: 1492.

Li Gotti MC, Saro S, Pergher G, Zucchiatti N, 2018. Uso sostenibile dei prodotti fitosanitari, Focus su tecniche di diserbo alternative al chimico. Notiziario Ersa 2:16-19.

Losavio GM, 2016. Dalla Caffini una nuovamacchina per l'idrodiserbo. Mondo macchina, novembre 2016.

Martelloni L, Frasconi C, Fontanelli M, Raffaelli M, Peruzzi A, 2016. Mechanical weed control on small-size dry bean and its response to cross-flaming. Span J Agric Res 14(1): e0203.

Nan L, Chunlong Z, Ziwen C, Zenghong M, Zhe S, Ting Y, Wei L, Junxiong Z, 2015. Crop positioning for robotic intra-row weeding based on machine vision. Int J Agric Biol Eng 8(6): 20-29.

Nørremark M, Griepentrog HW, Nielsen J, Søgaard HT, 2008. The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosyst Eng 101 (4): 396-410.

Nørremark M, Griepentrog HW, Nielsen J, Søgaard HT, 2012. Evaluation of an autonomous GPS-based system for intra-row weed control by assessing the tilled area. Precis Agric 13 (2): 149-162.

Perez-Jones A, Park KW, Colquhoun J, Mallory-Smith CA, Shaner D, 2005. Identification of glyphosate-resistant Italian ryegrass (Lolium multiflorum) in Oregon. Weed Sci 53: 775-779.

Pérez-Ruíz M, Slaughter DC, Fathallah FA, Gliever CJ, Miller BJ, 2014. Co-robotic intra-row weed control system. Biosyst Eng 126: 45-55.

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(2): 57-70.

Rasmussen J, Griepentrog HW, Nielsen J, Henriksen CB, 2012. Automated intelligent rotor tine cultivation and punch planting to improve the selectivity of mechanical intra-row weed control. Weed Res 52: 327-337.

Rastgordani F, Ahmadi A, Sajedi NA, 2013. The influence of mechanical and chemical methods on weeds control in maize. Tech J Eng Appl Sci 3-S: 3858-3863.

Rueda-Ayala V, Peteinatos G, Gerhards R, Andújar D, 2015. A non-chemical system for online weed control. Sensors 15: 7691-7707.

Salas RA, Dayan FE, Pan Z, Watson ZS, Dickson JW, Scott RC, Burgos NR, 2012. EPSPS gene amplification in glyphosate-resistant Italian ryegrass (Lolium perenne ssp. multiflorum) from Arkansas. Pest Manag Sci 68: 1223-1230.

Tahir I, Svensson SE, Hansson D, 2015. Floor management system in an organic apple orchard affect fruit quality and storage life. Hort Sci 50(3): 434-441.

Vander Weide RY, Bleeker PO, Achten VTJM, Lotz LAP, Fogelberg F, Melander B, 2008. Innovation in mechanical weed control in crop rows. Weed Res 48: 215-224.

Wisserodt E, Grimm J, Kemper M, Kielhorn A, Kleine-Hartlage H, Nardmann M, et al., 1999. Gesteuerte Hackezur Beikrautregulierung innerhalb der Reihe von Pflanzenkulturen. Proc VDI-Tagung Landtechnik Braunschweig, Dusseldorf, Germany. pp: 155-160.

How to Cite
AssirelliA., & LiberatiP. (2022). Simulation modelling of mechanical systems for intra-row weeding in a precision farming approach. Spanish Journal of Agricultural Research, 20(1), e0201.
Agricultural engineering