A Deep-learning Framework For Spray Pattern Segmentation And Estimation In Agricultural Spraying Systems

This article focuses on leveraging deep learning for agricultural applications, specifically for spray pattern segmentation and spray cone angle estimation. Three deep-learning convolution-based models are trained and compared, and the best model is used for spray region segmentation and spray cone angle estimation. The output from the model provides a binary image representing the spray region, which is further processed to estimate the spray cone angle. The validation process compares results obtained from this work with manual measurements.