Progress 10/01/19 to 09/30/20
Outputs
Target Audience:Growers, researchers, ag industry personnel, students andgeneral public. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?Outreach efforts for the precision spot sprayer aspects of this project are listed in the Accomplishments Section of this report. Results pertaining to automated, precision cultivator technology research conducted were disseminated to over 200 growers, ag industry personnel and researcher via presentations at field days extension meetings. Many more were reached through publishing ag industry articles (1) and on-line postings of presentations and devices in action videos (3) [> 400 views in 2019, YouTube]. What do you plan to do during the next reporting period to accomplish the goals?Conduct further performance tests of the precision spot sprayers developed. Continue efforts to commercialize the precision spot sprayer technologies developed. Seek funding to develop a computer imaging system that can differentiate between crop plants and weeds in lettuce production systems. The system will be utilized to provide a real-time weed targeting map for the precision spot sprayer developed. Continue our efforts to develop and integrate automated, precision cultivator technology into vegetable production systems.
Impacts
What was accomplished under these goals?
Progress was made on Objective 2. Automated and in-row weeding technologies were evaluated for weed control efficacy. Technologies examined included use of GPS-RTK guided tractors, camera-guided, self-steering cultivators and in-row weeding tools. Results showed that the flexible, rubber fingered in-row weeding tools tested were gentle enough on crops that they could be used on seedling (2-3 leaf stage) plants and that the camera-guidance system allowed cultivating tools to be positioned very close to the crop row (3.5" uncultivated band) without causing crop injury. Trial results showed that the finger weeding tools controlled 30-50% of the in-row weeds and that camera-guided, close cultivation improved weed control by more than 40%. Use of finger weeding tools in combination with camera-guided cultivators provided better than 85% weed control and resulted in less than 1/2 as many uncontrolled weeds as compared to conventional cultivation. Reducing weed density by such levels are expected to reduce hand weeding labor requirements by 33% in vegetable crops. A significant effort was made to achieve objective 3. In the first two years of this project, a high speed, centimeter scale resolution sprayer that can spot apply herbicides to weeds while traveling at speeds that are viable for commercial farming operations was developed and tested. During this review period, an extensive outreach effort was made to educate stakeholders and garner commercial interest in the technology. Over 500 individuals were reached via presentations at field days (1), extension/ag industry meetings (4) and by organizing workshops/tours/field days (2) and hosting student groups (2). Many more were reached through ag industry articles (3), and on-line postings of presentations (3) [> 250 views in 2019-2020, YouTube] and device in action videos (2) [> 250 views in 2019, YouTube]. This activity has led to being approached by seven start-up companies that are developing automated in-row weeding robots about commercializing the technology and asked to serve on advisory boards of two companies. Additionally, the technology was further developed so that it is capable of sub-centimeter scale resolution spot spraying at commercially viable speeds.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Raja, R., Slaughter, D.C., Fennimore, S.A., Nguyen, T.T., Vuong, V.L., Sinha, N., Tourte, L., Smith, R.F. & Siemens, M.C. 2019. Crop signaling: A novel crop recognition technique for robotic weed control. Biosystems Eng. 187: 278-291.
- Type:
Other
Status:
Published
Year Published:
2020
Citation:
Siemens, M.C. 2020. Machine Vision Systems for Automated Weeding Current Technologies and Future Directions. In Proc. 2020 Weed Sci. Soc. of America Ann. Meeting., Abstract no. 249. Maui, Hawaii: WSSA. Available at http://apexwebstudio.com/WeedSciAbstracts/WSSA/WSSA2020-Abstracts-Proceedings.html.
- Type:
Other
Status:
Published
Year Published:
2020
Citation:
Siemens, M.C. 2020. Videos on Precision Cultivation Technology and High Speed Centimeter Scale Resolution Sprayers. AZ Veg IPM Update. 13 May. Vol. 11, Issue 10. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.
- Type:
Other
Status:
Published
Year Published:
2020
Citation:
Siemens, M.C. 2020. Precision Cultivating Technologies for Improved Weed Control. AZ Veg IPM Update. 29 April. Vol. 11, Issue 9. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.
- Type:
Other
Status:
Other
Year Published:
2020
Citation:
Siemens, M.C. 2020. High Speed Centimeter Scale Resolution Sprayers for Precision Weed Control in Vegetable Crops. AZ Veg IPM Update. 15 April. Vol. 11, Issue 8. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.
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Progress 10/01/18 to 09/30/19
Outputs
Target Audience:Growers, researchers, ag industry personnel, students, general public Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?Over 500 individuals were reached via presentations at field days (5), on-farm demos (1), extension meetings (4) and scientific meetings (1) and by organizing workshops/tours/field days (3) and hosting student groups (1). Many more were reached through publishing peer reviewed (3) and scientific (1) articles and on-line postings of publications/presentations (>3,500 views in 2019, ResearchGate) and videos (>1,250 in 2019, YouTube). What do you plan to do during the next reporting period to accomplish the goals?In 2020, we plan to continue to develop, evaluate and extend information about high speed, 1-centimeter scale resolution spot sprayers. We also plan to continue our efforts to develop and integrate automated, precision cultivator technology into green chile production systems.
Impacts
What was accomplished under these goals?
Research was conducted on the effect of modified coring tool designs on iceberg lettuce cross-contamination. Results showed that modified tools of our own design reduced E. coli transfer by over 50%. Such designs can easily be implemented on automated coring machines. Study results were published in 2019. A project to identify parameters for optical detection of bird excrements in leafy green produce fields was completed. Results showed that reflectance imaging at around 500-525 nm or 600-620 nm could be used to reliably detect excrements from the three bird species tested. Fluorescence responses to UV illumination at 525 nm were also found to be an effective method for detecting bird droppings. The significance of these findings it that based on the parameters identified, inexpensive imaging systems could be developed and mounted on drones or ground based vehicles and used to help prevent the fecal contaminant from entering the food supply. Results were published in 2019. A high speed, 1-centimeter scale resolution sprayer that can spot apply herbicides to weeds while traveling at speeds that are viable for commercial farming operations was developed and tested. The performance of the device was evaluated in terms of spray delivery accuracy, off-target spray quantity, weed control efficacy and crop safety. The spray assembly comprised 12 custom-built spray assemblies spaced 1 cm apart. The device was tested with lettuce in the laboratory at a travel speed of 2.0 mph while targeting four weed species at four stages of growth. Results showed that targeting accuracy of spray delivered was ± 2 mm and that the percentage of off-target spray was less than 3%. Weed control efficacy exceeded 95% and there was no observable crop injury. The device was integrated with a real-time imaging system for detecting weed targets developed by project collaborators at UC Davis and tested in the lab. Results also indicated high levels of weed control with minimal crop injury. Results were presented at the 2019 ASABE Annual International Meeting.
Publications
- Type:
Journal Articles
Status:
Accepted
Year Published:
2019
Citation:
Raja, R., Slaughter, D.C., Fennimore, S.A., Nguyen, T.T., Vuong, V.L., Sinha, N., Tourte, L., Smith, R.F. & Siemens, M.C. 2019. Crop signaling: A novel crop recognition technique for robotic weed control. Biosystems Eng. (accepted).
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Govindaraj. D.K., Zhu, L., Siemens, M.C., Nolte, K.D., Brassill, N., Rios, D.V., Galvez, R., Fonseca, J.M. & Ravishankar, S. 2018. Modified Coring Tool Designs Reduce Iceberg Lettuce Cross-Contamination. J. Food Protection. 82(3): 454-462. DOI: 10.4315/0362-028X.JFP-18-317.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Lefcourt, A.M., Siemens, M.C. & Rivadeneira, P. 2018. Optical parameters for using visible-wavelength reflectance or fluorescence imaging to detect bird excrements in produce fields. Appl. Sci., 9(4), [715]; DOI:10.3390/app9040715.
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