Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h&minus:1), and spraying norm (250&ndash:400 L h&minus:1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%: radial = 66.0%), droplet diameter (axial = 30.6%: radial = 38.2%), and area coverage (axial = 24.6%: radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%: radial = 26.9%) and drift (axial = 72.4%: radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694&ndash:0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437&ndash:0.503).