Effect of Image Segmentation Thresholding on Droplet Size Measurement

• Published in: Agronomy (Basel) Vol. 12; no. 7; p. 1677
• Main Authors: Cerruto, Emanuele, Manetto, Giuseppe, Privitera, Salvatore, Papa, Rita, Longo, Domenico
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: air induction nozzle; Algorithms; Diameters; droplet atomization; droplet spectrum; Droplets; Environmental impact; Evaporation; Image acquisition; Image analysis; Image processing; Image segmentation; ImageJ; Inhalation; Laboratories; liquid immersion method; Measurement methods; Methods; Object recognition; Parameters; Pesticide application; Pesticides; Reproducibility; Respiration; Software; spray; Statistical analysis
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12071677

Droplet size spectrum is a key factor in pesticide application because it affects the biological efficacy of a treatment in terms of target coverage, environmental impact in terms of evaporation, drift and run-off, and operator’s safety in terms of inhalation and dermal exposure. Droplet measurement methods based upon image analysis have to face the “binarization” or “segmentation” process, by which the objects of interest (the droplets) are extracted from the background. Segmentation is carried out by choosing appropriate threshold values, mostly based on the operator’s experience. In this study, images of droplets of an air induction nozzle TVI 8002 at four pressures (0.3, 0.5, 1.0, and 1.5 MPa) were obtained using the liquid immersion method. Each image was processed multiple times, firstly by using a “reference” threshold value based on the operator’s experience and then by using 11 different threshold values, chosen in the range of around ±5% of the reference threshold and based upon the average gray level of the image. For each threshold value, the corresponding spray parameters (volumetric diameters, mean diameters, Sauter diameters, and numeric diameters) were analyzed. The results showed that spray parameters had a statistically significant linear trend with respect to the threshold values in most cases. However, in absolute terms, variations were almost always less than 1.0% of reference values. This result allows considering the image acquisition system used in the present study as an automatic tool able to select the threshold according to the gray level of the image, making the whole segmentation process faster, more objective, and less dependent on the operator’s experience.