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Accurate characterization of agricultural sprays is crucial to predict infield performance of liquid applied crop protection products. Here we introducea robust and efficient machine learning (ML) based Digital In-line Holography(DIH) to accurately characterize the droplet field for a wide range ofagricultural spray nozzles. Compared to non-ML methods, our method enhancesaccuracy, generalizability, and processing speed. Our approach employs twoneural networks: a modified U-Net to obtain the 3D droplet field from thenumerically reconstructed optical field, followed by a VGG16 classifier toreduce false positives from the U-Net prediction. The modified U-Net is trainedusing holograms generated using a single spray nozzle at three spray locations;center, half-span, and the spray edge to create training data with variousnumber densities and droplet size ranges. VGG16 is trained via the minimumintensity projection of the droplet 3D point spread function. Data augmentationis used to increase the efficiency of classification and make the algorithmgeneralizable for different measurement settings. The model is validated viaNIST traceable glass beads and six agricultural spray nozzles representingvarious spray characteristics. The results demonstrate a high accuracy rate,with over 90% droplet extraction and less than 5% false positives. Compared totraditional spray measurement techniques, our method offers a significant leapforward in spatial resolution and generalizability. In particular, our methodcan extract the real cumulative volume distribution of the NIST beads, wherethe laser diffraction is biased towards droplets moving at slower speeds.Additionally, the ML-based DIH enables the estimation of mass and momentum fluxat different locations and the calculation of relative velocities of dropletpairs, which are difficult to obtain via conventional techniques.

Visualization and Characterization of Agricultural Sprays Using Machine Learning based Digital Inline Holography