Location: Application Technology Research
Project Number: 5082-30500-002-000-D
Project Type: In-House Appropriated
Start Date: Feb 1, 2025
End Date: Jan 31, 2030
Objective:
Objective 1: Develop advanced sensor-vision guided intelligent precision technologies to efficiently apply pesticides and bio-products for efficacious and sustainable control of pest insects and other arthropods, diseases, and weeds, to protect horticultural, field, and greenhouse and other protected culture crops.
Sub-objective 1A: Develop an inline injection and mixing system, a greenhouse spray system, and a vegetable spray system to advance laser-guided intelligent spray applications.
Sub-objective 1B: Develop variable-rate sprayers with low-cost stereo vision and electric variable air assist systems to improve pesticide application efficiency.
Objective 2: Develop comprehensive methodologies for application of precision technologies in compliance with climate change effects to reduce pesticide waste, crop protection costs, chemical contaminations to the environment, and to protect workers, livestock, natural resources, and sensitive ecosystems.
Sub-objective 2A: Develop a spray drift simulation program for sensor-guided precision sprayers.
Sub-objective 2B: Investigate remote hyperspectral sensing technology for early detection of plant diseases in greenhouse environments.
Sub-objective 2C: Investigate optimal spray additives that can amend spray mixture physical properties to stabilize spray deposition on plants due to varied surface morphologies.
Approach:
A direct inline injection and mixing system will be developed as a retrofit module attached on sensor-guided variable-rate sprayers. Water and chemical concentrate will be stored in two separate tanks and will be instantaneously dispensed into the mixing line with desired amounts for target concentrations. Tests will be conducted to evaluate the system accuracy to discharge the desired spray mixture concentration and uniformity for variable rate spray applications.
An improved control system will be developed for horizontal spray booms to perform variable-rate applications in greenhouses. The system will be the integration of a new laser scanning sensor with a combination of an automatic flow control system and a spray boom containing PWM-controlled nozzles. Tests will validate the spray system accuracy to detect and spray plants but not spray empty space. Various-sized plants at different growth stages will be chosen for the tests. At the same time, a variable-rate spray system for field-grown vegetables will be designed by using the same laser scanning sensor, travel speed sensor, computer programs and flow control units used in the greenhouse variable-rate sprayer development.
A new prototype variable-rate sprayer will be developed using a low-cost commercial grade stereo vision system. The system will acquire depth images of tree canopy and convert them as tree canopy volume images to prescribe variable spray volumes accordingly. A new electric variable air assist system will also be developed to reduce spray off-target movement by adjusting the intensity of air assist for the prototype sprayer in real time. An algorithm for automatic control of the air assist system will be developed to increase or decrease the intensity of the air assist when the tree row has trees or no trees. The sprayer with the new electric air assist system will be evaluated in an experimental apple orchard for its functions to produce variable rates for both air and liquid.
A computer program based on a large spray drift database collected from CFD simulations will be developed to predict spray application efficiency and risk assessment for the sensor-guided variable-rate precision sprayers. The program will assist making decisions on spray applications in orchards, vineyards and nurseries.
An automated data collection system will be developed to determine the early detection of plant infection symptoms in a walk-in growth chamber. Tomatoes will be the target plants for tests. Hyperspectral radiometer will be used to collect electromagnetic spectrum reflected from leaf surfaces in the wavelength range from ultraviolet to short-wave infrared. A continuous machine learning model will be developed for data analyses.
Spray additives including drift retardants and adjuvants will be investigated to improve spray droplet retention and spread on plants and reduce spray drift potentials. A 3-D ultrahigh-speed video surveillance system will be used to investigate fading times and spread coverage areas of droplets, and dynamic effects of spray characteristics on the adhesion and spread of impacting droplets on leaves or plants.
