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Crop signal markers facilitate crop detection and weed removal from lettuce and tomato by an intelligent cultivator

  • HannahJoy Kennedy (a1), Steven A. Fennimore (a1), David C. Slaughter (a2), Thuy T. Nguyen (a2), Vivian L. Vuong (a2), Rekha Raja (a2) and Richard F. Smith (a3)...


Increasing weed control costs and limited herbicide options threaten vegetable crop profitability. Traditional interrow mechanical cultivation is very effective at removing weeds between crop rows. However, weed control within the crop rows is necessary to establish the crop and prevent yield loss. Currently, many vegetable crops require hand weeding to remove weeds within the row that remain after traditional cultivation and herbicide use. Intelligent cultivators have come into commercial use to remove intrarow weeds and reduce cost of hand weeding. Intelligent cultivators currently on the market such as the Robovator, use pattern recognition to detect the crop row. These cultivators do not differentiate crops and weeds and do not work well among high weed populations. One approach to differentiate weeds is to place a machine-detectable mark or signal on the crop (i.e., the crop has the mark and the weed does not), thereby facilitating weed/crop differentiation. Lettuce and tomato plants were marked with labels and topical markers, then cultivated with an intelligent cultivator programmed to identify the markers. Results from field trials in marked tomato and lettuce found that the intelligent cultivator removed 90% more weeds from tomato and 66% more weeds from lettuce than standard cultivators without reducing yields. Accurate crop and weed differentiation described here resulted in a 45% to 48% reduction in hand-weeding time per hectare.


Corresponding author

Author for correspondence: Steve Fennimore, University of California, Davis, Department of Plant Sciences, 1636 East Alisal, Salinas, CA93905 Email:


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Associate Editor: Michael Walsh, University of Sydney



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Boyd, NB, Brennan, EB, Fennimore, SA (2006) Stale seedbed techniques for organic vegetable production. Weed Technol 20:10521057
[CDFA] California Department of Food and Agriculture (2017) California Agricultural Statistics Review 2016–2017. pp 1–126 Accessed July 17, 2019
Christensen, S, Søgaard, HT, Kudsk, P, Nørremark, M, Lund, I, Nadimi, ES, Jørgensen, R (2009) Site-specific weed control technologies. Weed Res 49:233241
Fennimore, SA, Cutulle, M (2019) Robotic weeders can improve weed control options for specialty crops. Pest Manag Sci 75:17671774
Fennimore, SA, Doohan, DJ (2008) The challenges of specialty crop weed control, future directions. Weed Technol 22:364372
Fennimore, SA, Smith, RF, Tourte, L, LeStrange, M, Rachuy, JS (2014) Evaluation and economics of a rotating cultivator in bok choy, celery, lettuce, and radicchio. Weed Technol 28:176188
Fennimore, SA, Slaughter, DC, Siemens, MC, Leon, RG, Saber, MN (2016) Technology for automation of weed control in specialty crops. Weed Technol 30:823837
Fennimore, SA, Tourte, L, Rachuy, JS, Smith, RF, George, C (2010) Evaluation and economics of a machine-vision guided cultivation program in broccoli and lettuce. Weed Technol 24:3338
Fennimore, SA, Umeda, K (2003) Time of glyphosate application in glyphosate-tolerant lettuce. Weed Technol 17:738746
Gramig, G (2018) Weed management in organic crop cultivation. Pages 319334in Zimdahl, RL, ed. Integrated weed management for sustainable agriculture. 1st edn. Cambridge, UK: Burleigh Dodds Publishing
Kader, AA, Lipton, WL, Morris, LL (1973) Systems for scoring quality of harvested lettuce. HortSci 8:408409
Lati, RN, Siemens, MC, Rachuy, JS, Fennimore, SA (2016) Intrarow weed removal in broccoli and transplanted lettuce with an intelligent cultivator. Weed Technol 30:655663
Lechenet, M, Deytieux, V, Antichi, D, Aubertot, J-N, Bàrberi, P, Bertrand, M, Cellier, V, Charles, R, Colnenne-David, C, Dachbrodt-Saaydeh, S, Debaeke, P, Doré, T, Farcy, P, Fernandez-Quintanilla, C, Grandeau, G, et al. (2017) Diversity of methodologies to experiment Integrated Pest Management in arable cropping systems: Analysis and reflections based on a European network. Eur J Agron 83:8699
Melander, B, Lattanzi, B, Pannacci, E (2015) Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage. Crop Prot 72:18
Melander, B, Liebman, M, Davis, AS, Gallandt, ER, Bàrberi, P, Moonen, A-C, Rasmussen, J, van der Weide, R, Vidotto, F (2017) Non-Chemical Weed Management. Pages 245270 in Weed Research. Hoboken, NJ: John Wiley & Sons, Inc
Miyao, G, Aegerter, B, Sumner, D, Stewart, D (2017) Sample Costs to Produce Processing Tomatoes Sub-Surface, Drip Irrigated (Sdi) in the Sacramento Valley and Northern Delta 2017. Accessed July 27, 2019.
Pérez-Ruíz, M, Slaughter, DC, Fathallah, FA, Gliever, CJ, Miller, BJ (2014) Co-robotic intra-row weed control system. Biosyst Eng 126:4555
Raja, R, Slaughter, DC, Fennimore, SA, Nguyen, TT, Vuong, V, Sinha, N, Tourte, L, Smith, RF, Siemens, MC (2019a) Crop signaling: a novel crop recognition technique for robotic weed control. Biosyst Eng 187:278291
Raja, R, Slaughter, DC, Fennimore, SA (2019b) A novel weed and crop recognition technique for robotic weed control in a lettuce field with high weed densities. ASABE Annual International Meeting, Vol. 1 Accessed December 7, 2019
Raja, R, Slaughter, DC, Fennimore, SA (2019c) Precision weed control robot for vegetable fields with high crop and weed densities. ASABE Annual International Meeting, Vol. 1 Accessed December 7, 2019
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 52:327337
Slaughter, DC (2014) The biological engineer: sensing the difference between crops and weeds. Pages 7195in Young, SL, Pierce, FJ, eds. Automation: The Future of Weed Control in Cropping Systems. Dordrecht: Springer
Slaughter, DC, Giles, DK, Downey, D (2008a) Autonomous robotic weed control systems: A review. Comput Electron Agric 61:6378
Slaughter, DC, Giles, DK, Fennimore, SA, Nguyen, TT, Vuong, V, Neilson, L, Billing, R, Roach, JI, Vannucci, B (2019) Robotic Plant Care Systems and Methods. US Patent Application Publication. Pub. No. US 2019/0104722 A1
Slaughter, DC, Giles, DK, Fennimore, SA, Smith, RF (2008b) Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol 22:378384
Su, WH, Fennimore, SA, Slaughter, DC (2019) Fluorescence imaging for rapid monitoring of translocation behavior of systemic markers in snap beans for automated crop/weed discrimination. Biosyst Eng 186:156167
Taylor, AG, Salanenka, YA (2012) Seed treatments: phytotoxicity amelioration and tracer uptake. Seed Sci Res 22:S86S90
Tourte, L, Smith, RF, Klonsky, K, De Moura, R (2009) Sample costs to produce organic leaf lettuce: Central Coast Region 2009. Accessed July 18, 2019
Tourte, L, Smith, RF, Klonsky, K, Sumner, D, Gutierrez, C, Stewart, D (2015) Sample costs to produce and harvest romaine hearts Central Coast Region 2015. Accessed July 18, 2019
Tourte, L, Smith, RF, Murdock, J, Sumner, DA (2017) Sample Costs to Produce and Harvest Iceburg Lettuce Central Coast Region 2017. Accessed July 18, 2019
[USDA-NASS] United States Department of Agriculture–National Agricultural Statistics Service (2019) Vegetables 2018 summary. Accessed October 3, 2019
Van Der Weide, RY, Bleeker, PO, Achten, VT, Lotz, LA, Fogelberg, F, Melander, B (2008) Innovation in mechanical weed control in crop rows. Weed Res 48:215224
Yang, D, Avelar, SA, Taylor, AG (2018) Systemic seed treatment uptake during imbibition by corn and soybean. Crop Sci 58:20632070
Zahniser, S, Taylor, JE, Hertz, T, Charlton, D (2018) Farm labor markets in the United States and Mexico pose challenges for U.S. Agriculture. United States Department of Agriculture–Economic Research Service. Accessed November 1, 2019


Crop signal markers facilitate crop detection and weed removal from lettuce and tomato by an intelligent cultivator

  • HannahJoy Kennedy (a1), Steven A. Fennimore (a1), David C. Slaughter (a2), Thuy T. Nguyen (a2), Vivian L. Vuong (a2), Rekha Raja (a2) and Richard F. Smith (a3)...


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