A Novel Image-Based Method for Measuring Spray Pattern Distribution in a Mechanical Patternator

Abstract

The uniform distribution of pesticides via spraying is of crucial importance in achieving effective and environmentally sustainable crop protection. Conventional assessment techniques such as sensor-based patternators and electronic monitoring systems are often expensive, complex to calibrate, and limited in adaptability to different nozzle geometries or operating conditions. The present study introduces and validates a low-cost, image-based method as an alternative to the traditional volumetric approach for evaluating spray pattern uniformity in mechanical patternators. Spray tests were conducted under controlled laboratory conditions in order to minimize environmental variability and ensure repeatability. The present study compared two complementary methods—volumetric measurement and image analysis—to evaluate their agreement and accuracy in determining spray deposition profiles. The findings, which included correlation and multivariate tests, indicated a robust linear relationship between the two approaches (r = 0.990–0.999), with deviations falling below ±3% and no statistically significant multivariate differences (p = 0.067). The image-based approach effectively captured both central and edge regions of the spray pattern, demonstrating precision comparable to volumetric readings. The findings confirm that image analysis provides an accurate, reliable, and repeatable means of assessing spray uniformity without reliance on costly sensor technologies. The proposed method offers a practical and scalable alternative for laboratory calibration, nozzle classification, and research applications focused on optimizing agricultural spraying performance.