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Development of an Intelligent Sprayer to Optimize Pesticide Applications in Nurseries and Orchards

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2010, Doctor of Philosophy, Ohio State University, Food, Agricultural and Biological Engineering.

Variable rate spray applications using intelligent control systems can greatly reduce pesticide use and off-target contamination of environment in nursery and orchard productions. The current equipment with variable rate functions for tree crops is limited to ultrasonic sensor based control systems that only detect tree occurrence to switch nozzles on or off and measure tree width in low accuracy. However, the size and foliage density of canopies can vary greatly with trees even in the same orchard or nursery. The goal of this study was to develop an intelligently functioned sprayer prototype with utilization of a laser scanner controlled system to deliver variable-rate spray outputs that match the canopy sectional structures in real time, to improve pesticide spray efficiency without reducing the level of crop protection expected from pesticides.

A laser scanner was used to acquire data on the geometry, density characteristics and occurrence of the canopy on-the-go. A fast algorithm was developed to calculate tree canopy characteristic parameters. The algorithm also performed automatic detection of the ground surface, the tree row centerline, and tree width, height, volume and foliage density. A flow rate control unit was designed with Pulse Width Modulation (PWM) signals to adjust the flow rate from each individual nozzle in accordance with the detected tree sectional canopy size and density in real time. A back pressure bypass device was assembled to minimize the pressure fluctuations caused by operating solenoid valves. A mathematic model was developed to calculate the optimal spray rate.

Field tests were conducted in an apple orchard to spray trees at three growth stages for comparison of spray performances of three sprayers: the intelligent sprayer with automatic control, the intelligent sprayer without automatic control, and a conventional air blast sprayer.

Both laboratory and field tests verified that the algorithm developed for the intelligent sprayer was able to detect a wide variety of foliage canopy density changes, and the electro-mechanical controllers were fast enough to activate the sprayer to achieve desired coverage uniformity regardless of foliage canopy variations.

Field spray deposition tests in the orchard illustrated that compared to the intelligent sprayer without automatic control and the conventional air blast sprayer, the intelligent sprayer with automatic control did not produce excessive sprays inside tree canopies. Also, the intelligent sprayer with automatic control provided relatively uniform spray coverage and deposition inside canopies with different foliage densities at different growth stages. In addition, compared to the other two sprayers, the intelligent sprayer with automatic control reduced spray volume by 47% to 73% with much less off-target loss on the ground, through tree gaps and in the air.

By automatically spraying the optimal amount of spray mixtures into tree canopies and stopping spraying beyond target areas, the intelligent sprayer with automatic control can significantly reduce the amount and cost of pesticides for growers, reduce the risk of environmental pollution by pesticides, and provide safer and healthier working conditions for workers.

Erdal Ozkan, Ph.D. (Committee Chair)
Heping Zhu, Ph.D. (Committee Co-Chair)
Richard Derksen, Ph.D. (Committee Member)
Peter Ling, Ph.D. (Committee Member)
225 p.

Recommended Citations

Citations

  • Chen, Y. (2010). Development of an Intelligent Sprayer to Optimize Pesticide Applications in Nurseries and Orchards [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1290526778

    APA Style (7th edition)

  • Chen, Yu. Development of an Intelligent Sprayer to Optimize Pesticide Applications in Nurseries and Orchards. 2010. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1290526778.

    MLA Style (8th edition)

  • Chen, Yu. "Development of an Intelligent Sprayer to Optimize Pesticide Applications in Nurseries and Orchards." Doctoral dissertation, Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1290526778

    Chicago Manual of Style (17th edition)