Location: Application Technology Research
2021 Annual Report
Objectives
The long-term objective of this research is to advance spray applications with coordinated intelligent-decision technologies and strategies that enhance pesticide application efficiency and environmental stewardship for efficacious and affordable control of pest insects, diseases and weeds.
Objective 1: Develop intelligent precision technologies to efficiently apply pesticides and bio-products for efficacious and sustainable control of pest insects and arthropods, diseases and weeds to protect horticultural, field and greenhouse crops.
Sub-objective 1.1: Develop a reliable and user-friendly intelligent spray-decision system as a retrofit for new and existing air-assisted sprayers to deliver pesticides and bio-products accurately, economically, and environmentally for field specialty crops.
Sub-objective 1.2: Develop greenhouse intelligent spray systems for real-time control of individual nozzle outputs to improve spray deposition quality and reduce waste of water and chemicals.
Objective 2: Develop coordinated application methodologies to reduce pesticide use, reduce crop protection costs, reduce chemical contaminations to the environment, and protect workers, livestock, natural resources and sensitive ecosystems.
Sub-objective 2.1: Improve spray droplet fading process to maximize coverage area after deposition on plants through coordinating spray parameters including droplet size, formulation physical properties, plant surface morphology, and ambient air conditions.
Sub-objective 2.2: Improve spray droplet retention and reduce runoff on plants through coordinating the influences of droplet size and velocity, travel speed, spray formulation physical properties, crop leaf surface morphology, and leaf surface orientation on dynamic impact, retention, rebound and spread process of spray droplets on plants.
Approach
A versatile intelligent spray control system and mounting kits will be developed as a retrofit to different types of tractor-driven sprayers to deliver pesticides and bio-products for different specialty crops. A microprocessor controlled premixing inline injection module will be developed and integrated into the versatile spray control system. Performance of these sprayers will be tested for their accuracy to manipulate spray deposition, spray drift, off-target loss and spray volume consumption in comparison with conventional sprayers. Efficacy tests will be conducted in nurseries, apple orchards and vineyards to compare pest control, pesticide quantity used, and cost savings for the sprayers with and without intelligent functions. Spray drift models will be developed to predict movement of droplets discharged from conventional and intelligent sprayers under nursery, orchard and vineyard conditions.
Greenhouse intelligent spray systems will be developed for real-time control of individual nozzle outputs to improve spray deposition quality and reduce waste of water and chemicals. The automatic greenhouse spray system will be a retrofit attached to existing watering booms. Laboratory tests will be conducted to validate the spray control system accuracies in spray delay time, nozzle activation and spray volume using artificial objects of different regular geometric shapes and surface textures, and artificial plants of different canopy structures. Spray deposition and pest control efficacy tests in greenhouses will then be conducted to validate the intelligent spray control system.
Microscopic spray droplet spreading times and areas on leaves will be investigated to maximize and stabilize coverage area after deposition on plants. Investigation parameters include droplet size, formulation physical properties, plant surface morphology, and ambient air conditions. Droplet fading rate, absorption rate and residual pattern coverage area will be measured on the waxy, semi-waxy and hairy leaf surfaces, and hydrophilic and hydrophobic glass slide surfaces. Field experiments will be conducted in ornamental nurseries, orchards, greenhouses, vegetables, traditional crops and weeds to verify laboratory discoveries effects of the most influenced factors on droplet spreading areas.
Dynamic effects of spray parameters on the droplet impact, rebound, retention, adhesion, and spread process on plants will be determined. The parameters are droplet size and velocity, travel speed, spray formulation type, and leaf surface morphology and orientation.
Significance of coordinating these parameters to improve spray droplet retention and reduce runoff on plants will be analyzed. Dynamic impact of water-based droplets on plant leaves will also be investigated in a wind tunnel under controlled conditions.
Progress Report
To achieve Objective 1.1, the following progress was made: (1) A new high-speed laser sensor was tested to measure plant canopy architectures, foliage density and presence to improve the durability and reliability under harsh field conditions for orchards, vineyards and nurseries. The sensor was used in the commercial intelligent spray control system to detect plants on both sides of the sprayer. It releases 54,000 detection signals per second with a 270-degree and 164-ft radial detection range to determine the presence of a plant canopy and measure the canopy height, width, foliage density, and canopy foliage volume. (2) An inexpensive indoor-use radial laser sensor along with a newly developed point cloud data processing algorithm was investigated to measure greenhouse plant surface profiles to improve the accuracy of a previously developed system. Evaluations included four objects of different regular geometrical shapes, and artificial and real plants. Three-dimensional images for the object surfaces were reconstructed with the data acquired from the laser sensor at various detection heights and sensor travel speeds. Plant canopy characterization was improved through the algorithm by isolating individual targets from the dataset, removing distortion, and estimating the occluded portion of the plant canopies. The point cloud data processing algorithm for the laser sensor increased the range of accurate measurements and reduced time in mapping large areas.
To achieve Objective 1.2, the following progress was made: (1) A processing algorithm was developed for the indoor-use laser sensor to manipulate the noisy dataset and determine the optimal sensor height to produce better measurement of greenhouse crops. The algorithm was a combination of registration, clustering and mirroring algorithms. The performance of the processing algorithm was evaluated by calculating the root mean square error in the canopy width measurements. The processing algorithm reduced errors by 46% and the largest improvements were seen for objects placed beyond 1.5 m from the sensor. Another experimental setup was used to test the limits of the relation between sensor height and the algorithm performance while using objects that were more representative of plant canopy shapes. (2) An experimental spray system for greenhouse applications was developed for real-time control of individual nozzle outputs. The system mainly consisted of a high-speed laser scanning sensor, 12 individual variable-rate nozzles, an embedded computer, a spray control unit, and a 3.6 m long mobile spray boom. Each nozzle was coupled with a pulse width modulated solenoid valve to discharge at variable rates based on object presence and plant canopy structures. Laboratory tests were conducted to evaluate the spray control system accuracy with regard to spray delay time, nozzle activation, and spray volume using four objects of different regular geometrical shapes and surface textures, and two artificial plants of different canopy structures. (3) A new algorithm called “Permanent Crop Analyzer” was developed to map plant canopy architectures for orchards, nursery and vineyards. Functions of the algorithm included tree counting, tree size, foliage density heat map comparison capability, liquid volume sprayed per plant, maps of sprayed plant locations, cloud sync feature, web portal for configuration settings and spray coverage report view, system log files, five different languages (English, Spanish, French, German and Italian), and options for choosing metric or British unit.
To achieve Objective 2.1, the following progress was made: A commercially-available 3D optical surface profiler was tested for its accuracy to quantify leaf surface fine structures. Measurement accuracy was validated by a qualitative visual analysis comparing 3D surface renderings of leaf surfaces generated by the profiler and micrographs from a scanning electron microscope. Test results showed visual agreement in surface variations from waxes and trichomes. Additional accuracy validation of the 3D profiler was carried out using sub-micrometer scaled roughness on leaves with hierarchical (multi-scale) structuring and smooth surfaces by comparing its measurements with the micrometer measurements. The length of measured roughness for the multi-scale and smooth surfaces were on the same order of magnitude for micrometer scale roughness, approximately 1, but different orders of magnitude for sub-micrometer scale roughness, approximately 0.1 and 0.001, respectively. The results of sub-micrometer scale surface roughness measurements from the micrometer and the 3D profiler were then used to investigate droplet behaviors on leaves applied with different adjuvants. Leaf surface fine structures were investigated using the profiler and leaf surface fine structures were characterized by mean roughness length, skewness, and kurtosis. Their values were compared with the wettability of seven leaf types ranging from easy-to-wet to very difficult-to-wet.
To achieve Objective 2.2, the following progress was made: (1) An improved 3D ultrahigh-speed video system was developed to elucidate mechanisms of droplet dynamic impact processes on leaves to increase pesticide spray application efficiency. Droplet size and initial travel speed were controlled by a streamed mono-sized droplet generator mounted to a variable speed linear track. Droplet impact measurements were made by emitting droplets from the droplet generator as it passed over a leaf sample mounted to a glass slide resting on a raised horizontal platform. Droplet size, impact, rebound and retention on the leaves were captured with a 3D stereoscopic imaging system consisting of three ultrahigh-speed digital cameras and analyzed with 3D motion analysis software. A new 3D optical surface profiler was used to measure the arithmetic mean roughness length to provide a reliable metric for leaf surface roughness. With this system, the main variables such as droplet sizes, droplet travel speeds, leaf surface structures and chemical formulations could be individually examined to establish a large database of droplet behaviors for future spray application strategies. (2) Preliminary tests were conducted to examine the relationship between leaf surface roughness and droplet deposition by correlating droplet adhesion with the surface roughness of six leaf types for spray solutions at five surfactant concentrations and eight initial droplet horizontal travel speeds. Leaf roughness and wettability in terms of contact angle ranged from smooth and easy-to-wet to rough and difficult-to-wet. Deposition was determined by comparing initial droplet volume to the residual liquid volume after impact. High surfactant concentrations were required to achieve deposition for the roughest leaves. (3) Droplet retention and spread behaviors with seven commercially available adjuvants were tested and compared at different concentrations. Tests were conducted with three leaf surfaces ranging in roughness and wettability, from very smooth and hydrophilic to very rough and superhydrophobic. The adjuvants were formulated with non-ionic surfactant, crop oil, seed oil, organo-silicone, hydrocolloid polymer, or combinations of these agents as functional ingredients.
Accomplishments
1. Commercialization of intelligent spray system for specialty crops. A commercial version of the intelligent spray system was jointly developed by ARS scientists at Wooster, Ohio, and Smart Guided Systems LLC. The new system included a new laser sensor, a tablet, a GPS navigator, an automatic flow rate controller, an air filtration unit, a toggle switch box, and a universal mounting kit. Features in the system included tree counting, tree size, foliage density heat map comparison capability, liquid volume sprayed per plant, maps of sprayed plant locations, ability to turn nozzles on/off independently through the tablet screen, cloud sync feature, web portal for configuration settings and spray coverage report view, system log files, five different languages (English, Spanish, French, German and Italian), and options for choosing metric or British unit. The commercial products have been used as a retrofit kit on existing sprayers by growers in the U.S. and other countries with crops including citrus, nursery, pecan, blueberry, peach, almond, apple, pear and grape. Pesticide usage is reduced in the range between 30 to 85% depending on crop types and growth stages. John Deere sells the commercial intelligent spray control system for use in high value crop applications through their dealer network. This environmentally-sustainable product received the 2021 SIMA Gold Metal which is the largest Agricultural Innovation award in Europe. Also, a new product “Permanent Crop Analyzer”, a part of the commercial intelligent spray system, was awarded as the 2021 Top-10 New Product winners by World Ag Expo.
Review Publications
Zhang, Z., Zhu, H., Hu, C. 2020. Hardware and software design for premixing in-line injection system attached to variable-rate orchard sprayer. Transactions of the ASABE. 63(4): 823-831. https://doi.org/10.13031/trans.13730.
Zhang, Z., Zhu, H., Salcedo, R., Wei, Z. 2020. Assessment of premixing in-line injection system attached on variable-rate orchard sprayer. Journal of ASTM International, Pesticide Formulation and Delivery Systems. 4:11-24. https://doi.org/10.1520/stp162720190120.
Chen, L., Zhu, H. 2020. Evaluation of laser-guided intelligent sprayer to control insects and diseases in ornamental nurseries and fruit farms. Journal of ASTM International, Pesticide Formulation and Delivery Systems. 40:1-10. https://doi.org/10.1520/stp162720190151.
Abbott, J.R., Ambrose, A.E., Zhu, H. 2020. Effects of leaf surface roughness and five adjuvants types on impacting droplet adhesion and spread. Journal of ASTM International, Pesticide Formulation and Delivery Systems. 40:128-139. https://doi.org/10.1520/stp162720190145.
Mahmud, M., Zahid, A., He, L., Choi, D., Krawczyk, G., Zhu, H., Heinemann, P. 2021. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications. Computers and Electronics in Agriculture. 182. Article 106053. https://doi.org/10.1016/j.compag.2021.106053.
Abbott, J.R., Zhu, H., Ambrose, A.E. 2020. Impact and adhesion of surfactant-amended water droplets on leaf surfaces related to roughness. Transactions of the ASABE. 63(6):1855-1868. https://doi.org/10.13031/trans.14027.
Boatwright, H., Zhu, H., Clark, A.C., Schnabel, G. 2020. Evaluation of the Intelligent Sprayer System in peach production. Plant Disease. 104(12):3207-3212. https://doi.org/10.1094/PDIS-04-20-0696-RE.
Zhang, Z., Zhu, H., Salcedo, R., Wei, Z. 2020. Assessment of chemical concentration accuracy and mixture uniformity of premixing in-line injection system. Computers and Electronics in Agriculture. 176. Article 105670. https://doi.org/10.1016/j.compag.2020.105670.
Wei, Z., Zhu, H., Zhang, Z., Salcedo, R. 2021. Droplet size spectrum, activation pressure and flow rate discharged from PWM flat-fan nozzles. Transactions of the ASABE. 64(1):313-325. https://doi.org/10.13031/trans.14100.
Salcedo, R., Zhu, H., Zhang, Z., Wei, Z., Chen, L., Ozkan, E., Falchieri, D. 2020. Foliar deposition and coverage on young apple trees with PWM controlled spray systems. Computers and Electronics in Agriculture. 178. Article 105794. https://doi.org/10.1016/j.compag.2020.105794.
Warneke, B., Zhu, H., Pscheidt, J., Nackley, L. 2020. Canopy spray application technology in specialty crops: A slowly evolving landscape. Pest Management Science. 77(5):2157–2164. https://doi.org/10.1002/ps.6167.
Chen, L., Zhu, H., Horst, L., Wallhead, M., Reding, M.E., Fulcher, A. 2020. Management of pest insects and plant diseases in fruit and nursery production with laser-guided variable-rate sprayers. HortScience. 56(1):94–100. https://doi.org/10.21273/HORTSCI15491-20.
Guler, H., Zhang, Z., Zhu, H., Grieshop, M., Ledebuhr, M. 2020. Spray characteristics of rotary micro sprinkler nozzles used in orchard pesticide delivery. Transactions of the ASABE. 63(6):1845-1853. https://doi.org/10.13031/trans.13445.
Fessler, L., Fulcher, A., Lockwood, D., Wright, W., Zhu, H. 2020. Advancing sustainability in tree crop pest management: refining spray application rate with a laser-guided variable-rate sprayer in apple orchards. HortScience. 55(9):1522–1530. https://doi.org/10.21273/HORTSCI15056-20.
