Deep CNN based droplet deposition segmentation for spray distribution assessment

Pesticides have been widely used in the cultivation of crops to enhance their production, however, incorrect application of pesticides will result in yield loss, product waste, environmental pollution among many others. Therefore, timely evaluating spray distribution of intelligent sprayers plays a pivotal role in the appropriate delivery of pesticides to the crop. The exiting approaches based on water-sensitive paper (WSP) either involve a relatively tedious and labor-intensive procedure, or have a high requirement on WSP image taking. So in this study we aim to conduct spray distribution assessment in the field based on mobile devices. To this end, the key issue of droplet deposition segmentation under natural imaging environments is addressed. WSPs with food dye droplets are first collected in the field by mobile phones. Then an image dataset on droplet deposition segmentation is created via thresholding approach with human supervision. Then four popular deep convolutional neural network (CNN) based segmentation algorithms are applied for droplet deposition segmentation so that spray distribution can be assessed. Comparative experiments show that UNeXt network is the best one in consideration of accuracy, inference time and network size.