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Deep CNN based droplet deposition segmentation for spray distribution assessment

conference contribution
posted on 17.06.2022, 08:10 authored by Tao Chen, Yanhua MengYanhua Meng, Jinya Su, Cunjia LiuCunjia Liu

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.


Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms

Science and Technology Facilities Council

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Space-enabled Crop disEase maNagement sErvice via Crop sprAying Drones (SCENE-CAD)

Science and Technology Facilities Council

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Royal Society grant No. IEC\NSFC\191320

2021 Anyang Science and Technology Project project No. 2021C01NY037



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

Published in

2022 27th International Conference on Automation and Computing (ICAC)


2022 27th International Conference on Automation and Computing (ICAC2022)


Institute of Electrical and Electronics Engineers (IEEE)


AM (Accepted Manuscript)

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date





Bristol, United Kingdom

Event dates

1st September 2022 - 3rd September 2022


Dr Yanhua Meng. Deposit date: 15 June 2022