Computational model of pesticide deposition distribution on canopies for air-assisted spraying

Front Plant Sci. 2023 May 8:14:1153904. doi: 10.3389/fpls.2023.1153904. eCollection 2023.

Abstract

The deposited pesticide distribution in fruit tree canopies is crucial for evaluating the efficacy of air-assisted spraying in orchards. Most studies have determined the impact of pesticide application on pesticide deposition on canopies without a quantitative computational model. In this study, an air-assisted orchard sprayer with airflow control was used to perform spraying experiments on artificial and peach trees. In the spraying experiment on an artificial tree, a canopy with leaf areas ranging from 2.54~5.08 m2 was found to require an effective air speed of 18.12~37.05 m/s. The canopy leaf area, air speed at the sprayer fan outlet and spray distance were used as test factors in a three-factor five-level quadratic general rotational orthogonal test to develop a computational model for pesticide deposition at the inner, outer and middle regions of a fruit tree canopy with R 2 values of 0.9042, 0.8575 and 0.8199, respectively. A significance analysis was used to rank the influencing factors for the deposited pesticide distribution in decreasing order of significance as follows: the spray distance, leaf area and air speed for the inner region of the canopy, followed by the spray distance, air speed and leaf area for the middle and outer regions of the canopy. The results of the verification test conducted in a peach orchard showed that the computational errors of the pesticide deposition model for the inner, middle and outer regions of the canopy were 32.62%, 22.38% and 23.26%, respectively. The results provide support for evaluating the efficacy of an air-assisted orchard sprayer and optimizing the sprayer parameters.

Keywords: air-assisted spraying; canopy; computational model; crop protection; pesticide deposition.

Grants and funding

This work was financially supported by the National Key Research and Development Plan Project (grant number: 2022YFD2001402), the Jiangsu Province and Education Ministry Cosponsored Synergistic Innovation Center of Modern Agricultural Equipment (grant number: XTCX1002), the Outstanding Scientist Program of Beijing Academy of Agriculture and Forestry Sciences (grant number: jkzx202212) and the Natural Science Foundation of China (grant number: NSFC31971775).