While much of modern agriculture is based on mass mechanized production, advances in sensing and manipulation technologies may facilitate precision autonomous operations that could improve crop yield and quality while saving energy, reducing manpower, and being environmentally friendly. In this paper, we focus on autonomous spraying in vineyards and present four machine vision algorithms that facilitate selective spraying. In the first set of algorithms we show how statistical measures, learning, and shape matching can be used to detect and localize the grape clusters to guide selected application of hormones to the fruit, but not the foliage. We also present another algorithm for the detection and localization of foliage in order to facilitate precision application of pesticide. All image-processing algorithms were tested on data from movies acquired in vineyards during the growing season of 2008 and their evaluation includes analyses of the potential pesticide and hormone reduction. Results show 90% accuracy of grape cluster detection leading to 30% reduction in the use of pesticides. The database of images is placed on the Internet and available to the public to continue developing the detection algorithms.
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Pimentel D, Lehman H (1993) The pesticide question: environment, economics, and ethics. Chapman & Hall, London
Tardiff RG (1992) Methods to assess adverse effects of pesticides on non-target organisms. Wiley, London
Jeyaratnam J (1990) Acute pesticide poisoning: a major global health problem. World Health Stat Q 43(3): 139–144
Koh D, Jeyaratnam J (1996) Pesticides hazards in developing countries. Sci Total Environ 188(1): 78–85
Ganzelmeier H (2006) Plant protection and plant cultivation. Yearbook of Agriculture Engineering, pp 101-109
Stentz A, Dima C, Wellington C, Herman H, Stager D (2002) A system for semi-autonomous tractor operations. Autonomous Robots 13(1): 87–104
Gillis KP, Giles DK, Slaughter DC, Downey D (2001) Injection and fluid handling system for machine-vision controlled spraying. ASAE Paper No.011114, ASAE, St. Joseph, MI 49085
Balsari P, Oggero G, Tamagnone M (2000) Evaluation on different pesticide distribution techniques on apple orchards [Malus pumila Mill.-Piedmont]. Italian Phytopathological Society. Biennial meeting, Perugia
Manor G, Gal Y, Chicago HR (2002) Development of an accurate vineyard sprayer. ASAE Paper No.02-1033, ASAE, St. Joseph, MI 49085
Wiedemann HT, Ueckert DN, McGinty WA (2002) Spray boom for sensing and selectively spraying small mesquite on highway rights-of-way. Appl Eng Agric 18(6): 661–666
Steward BL, Tian LF, Tang L (2002) Distance-based control system for machine vision-based selective spraying. Trans ASAE 45(5): 1255–1262
Nishiwaki K, Amaha K, Otani R (2004) development of nozzle positioning system for precision sprayer. Automation Technology for Off-Road Equipment, Kyoto
Zheng J (2005) Intelligent pesticide spraying aims for tree target, Resource, September, St. Joseph, MI: ASABE
Shin BS, Kim SH, Park JU (2002) Autonomous agricultural vehicle using overhead guide. Automation Technology for Off-Road Equipment, Chicago
Ogawa Y, Kondo N, Monta M, Shibusawa S (2006) Spraying robot for grape production. Springer, Berlin
Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61(1): 63–78
Lamm RD, Slaughter DC, Giles DK (2002) Precision weed control system for cotton. Trans SAE 45(1): 231–238
Lee WS, Slaughter DC, Giles DK (1999) Robotic weed control system for tomatoes. Precis Agric 1(1): 95–113
Shapiro A, Korkidi E, Demri A, Ben-Shahar O, Riemer R, Edan Y (2009) Toward elevated agrobotics: Development of a scaled-down prototype for visually guided date palm tree sprayer. J Field Robotics 26(6): 572–590
Younse P, Burks T (2005) Intersection detection and navigation for an autonomous greenhouse sprayer using machine vision, ME thesis. University of Florida, Department of Agricultural and Biological Engineering, Gainesville, Fla.
Jeon,HY, Tian LF, Grift T (2005) Development of an individual weed treatment system using a robotic arm. ASAE Paper No.05-1004, ASAE St. Joseph, MI 49085
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(1): 679–698
Sharifi M, Fathy M, Mahmoudi MT (2002) A classified and comparative study of edge detection algorithms. In: International conference on information technology (ITCC), Las Vegas
Shin MC, Goldgof D, Bowyer KW (1998) An objective comparison methodology of edge detection algorithms using a structure from motion task. In: Conference on computer vision and pattern recognition, Santa Barbara
Breiman L (1984) Classification and regression trees. Chapman & Hall/CRC,
Grasso G, Recce M (1996) Scene analysis for an orange picking robot. In: Congress for computer technology in agriculture (ICCTA), Wageningen
Juste F, Sevila F (1991) Citrus: a European project to study the robotic harvesting of oranges. In: Workshop on robotics in agriculture and the food industry, Genova
Pla F, Juste F, Ferri F, Vicens M (1993) Colour segmentation based on a light reflection model to locate citrus fruits for robotic harvesting. Comput Electr Agric 9(1): 53–70
Sites PW, Delwiche MJ (1985) Computer vision to locate fruit on a tree. ASAE Paper No. 85-3039, ASAE, St. Joseph, MI 49085
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Berenstein, R., Shahar, O.B., Shapiro, A. et al. Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer. Intel Serv Robotics 3, 233–243 (2010). https://doi.org/10.1007/s11370-010-0078-z
- Precision agriculture
- Image processing
- Edge detection
- Decision tree
- Machine learning