Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-28T07:01:31.557Z Has data issue: false hasContentIssue false

Functionality and efficacy of Franklin Robotics’ Tertill robotic weeder

Published online by Cambridge University Press:  24 August 2020

Johnny Sanchez*
Affiliation:
Graduate Research Assistant, Ecology and Environmental Sciences, University of Maine, Orono, ME, USA
Eric R. Gallandt
Affiliation:
Professor of Weed Ecology, School of Food and Agriculture, University of Maine, Orono, ME, USA
*
Author for correspondence: Johnny Sanchez, Deering Hall, Grove St Ext, Orono, ME04473. Email: johnny.sanchez@maine.edu

Abstract

Agricultural weeds remain an important production constraint, with labor shortages and a lack of new herbicide options in recent decades making the problem even more acute. Robotic weeding machines are a possible solution to these increasingly intractable weed problems. Franklin Robotics’ Tertill is an autonomous weeding robot designed for home gardeners that relies on a minimalistic design to be cost-effective. The objectives of this study were to investigate the ability of the Tertill to control broadleaf and grass weeds, and based on early observations, experiments were conducted with and without its string-trimmer–like weeding implement. Tertill demonstrated high weed-control efficacy, supporting its utility as a tool for home gardeners. Weeds were best controlled by the combined effect of soil disturbance caused by the action of the robot’s wheels and the actuation of the string trimmer. Despite the regrowth potential of an annual grass due to its meristem location, Tertill maintained low densities of millet in an experimental arena. The simple and effective design of the Tertill may offer insights to inform future development of farm-scale weeding robots. Weed density, emergence periodicity, robot working rate, and robotic weeding mechanisms are important design criteria regardless of the technology used for plant detection.

Type
Note
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the Weed Science Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Associate Editor: Steve Fennimore, University of California, Davis

References

Blender, T, Buchner, T, Fernandez, B, Pichlmaier, B, Schlegel, C (2016) Managing a mobile agricultural robot swarm for a seeding task. IECON 2016: 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence Italy, October 23–26CrossRefGoogle Scholar
Brown, B, Gallandt, E (2018) Evidence of synergy with ‘stacked’ intrarow cultivation tools. Weed Res 58:18 CrossRefGoogle Scholar
Cardina, J, Johnson, G, Sparrow, D (1997) The nature and consequence of weed spatial distribution. Weed Sci 45:364373 CrossRefGoogle Scholar
Crowder, D, Jabbour, R (2014) Relationships between biodiversity and biological control in agroecosystems: current status and future challeneges. Biol Control 75:817 CrossRefGoogle Scholar
Davis, AS, Frisvold, GB (2017) Are herbicides a once in a century method of weed control? Pest Manag Sci 73:22092220 CrossRefGoogle Scholar
Duke, S (2012) Why have no new herbicide modes of action appeared in recent years. Pest Manag Sci 68:505512 CrossRefGoogle ScholarPubMed
Egley, G, Williams, R (1991) Emergence periodicity of six summer annual weed species. Weed Sci 39:595600 CrossRefGoogle Scholar
Evans, GJ, Bellinder, RR, Hahn, RR (2012) An evaluation of two novel cultivation tools. Weed Technol 26:316325 CrossRefGoogle Scholar
Fennimore, SA, Cutulle, M (2019) Robotic weeders can improve weed control options for specialty crops. Pest Manag Sci 75:17671774 CrossRefGoogle ScholarPubMed
Fennimore, SA, Slaughter, DC, Siemens, MC, Leon, RG, Saber, MN (2016) Technology for automation of weed control in specialty crops. Weed Technol 30:823837 CrossRefGoogle Scholar
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 CrossRefGoogle Scholar
Gallandt, E (2010) Evaluation of scale-appropriate weed control tools for the small farm. ONE09-098. Northeast SARE. https://projects.sare.org/project-reports/one09-098/. Accessed: September 20, 2020Google Scholar
Gallandt, E, Brainard, D, Brown, B (2018) Developments in physical weed control. Pages 123 in Integrated Weed Management for Sustainable Agriculture. Burleigh Dodds Science Publishing Google Scholar
Gallandt, E, Weiner, J (2007) Crop-weed competition. Pages 18 in Zimdahl, RL, ed. Encyclopdeia of Life Sciences. Hoboken, NJ: John Wiley & Sons, Ltd.Google Scholar
Grimstad, L, Pham, CD, Phan, HT, From, PJ (2015) On the design of a low-cost, light-weight, and highly versatile agricultural robot. Pages 1–6 in 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts. Piscataway, NJ: IEEE-Robotics and Automation Society Google Scholar
Jabbour, R, Gallandt, E, Zwickle, S, Wilson, R, Doohan, D (2014) Organic farmer knowledge and perceptions are associated with on-farm weed seedbank densities in northern New England. Weed Sci. 62:338349 Google Scholar
Jackson, LE, Ramirez, I, Yokota, R, Fennimore, SA, Koike, ST, Henderson, DM, Klonsky, K (2004) On-farm assessment of organic matter and tillage management on vegetable yield, soil, weeds, pests, and economics in California. Agric Ecosyst Environ 103:443463 CrossRefGoogle Scholar
Kurstjens, D, Kropff, M, Perdok, U (2004) Method for predicting selective uprooting by mechanical weeders from plant anchorage forces. Weed Sci 52:123132 CrossRefGoogle Scholar
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 CrossRefGoogle Scholar
Lindquist, J, Dieleman, A, Mortensen, D, Johnson, G, Wyse-Pester, D (1998) Economic importance of managing spatially heterogeneous weed populations. Weed Technol 12:713 CrossRefGoogle Scholar
McAllister, W, Osipychev, D, Davis, A, Chowdhary, G (2019). Agbots: weeding a field with a team of autonomous robots. Comp Electron Agric 163:104827 CrossRefGoogle Scholar
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 CrossRefGoogle Scholar
Merfield, CN (2016) Robotic weeding’s false dawn? Ten requirements for fully autonomous mechanical weed management. Weed Res 56: 340344 CrossRefGoogle Scholar
Mohler, C (1996) Ecological bases for the cultural control of annual weeds. J Prod Agric. 9:468474 CrossRefGoogle Scholar
Naïo, Technologies (2020) Dino’s brand new mechanical weeding service: WAAS! https://www.naio-techologies.com. Accessed: June 11, 2020Google Scholar
Olsen, J, Kristensen, L, Weiner, J, Kristensen, L, Weiner, J (2005) Species effects of density and spatial pattern of winter wheat on suppression of different weed species density and pattern when weeds were controlled by herbicide were also investigated. Weed Sci. 53:690694 CrossRefGoogle Scholar
Pérez-Ruiz, M, Slaughter, DC, Gliever, CJ, Upadhyaya, SK (2012) Automatic GPS-based intra-row weed knife control system for transplanted row crops. Comput Electron Agric 80:4149 CrossRefGoogle Scholar
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 48:5770 Google Scholar
Rasmussen, J (1991) A model for prediction of yield response in weed harrowing. Weed Res 31:401408 CrossRefGoogle Scholar
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 CrossRefGoogle Scholar
Stoller, E, Wax, L (1973) Periodicity of germination and emergence of some annual weeds. Weed Sci 21:574580 CrossRefGoogle Scholar
Tillet, ND, Hague, T, Grundy, AC, Dedousis, AP (2007) Mechanical within-row weed control for transplanted crops using computer vision. Biosyst Eng. 99:171178 CrossRefGoogle Scholar
Utstumo, T, Urdal, F, Brevik, A, Dørum, J, Netland, J, Overskeid, Ø, Tommy, J (2018) Robotic in-row weed control in vegetables. Comput Electron Agric 154:3645 CrossRefGoogle Scholar
Vanhala, P, Kurstjens, D, Ascard, J, Bertram, A, Cloutier, DC, Mead, A, Raffaelli, M, Rasmussen, J (2004) Guidelines for physical weed control research: flame weeding, weed harrowing and intra-row cultivation. 6th EWRS Workshop on Phyical and Cultural Weed Control, Lillehammer, Norway, March 8–10 2004Google Scholar
Yunez-Naude, A, Taylor, JE, Charlton, D (2012) The end of farm labor abundance. Appl Econ Perspect Policy 34:587598 Google Scholar