An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection
The rapid development of vision sensor based on artificial intelligence (AI) is reforming industries and making our world smarter. Among these trends, it is of great significance to adapt AI technologies into the intelligent agricultural management. In smart agricultural aviation spraying, the dropl...
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MDPI AG
2019-02-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/19/4/933 |
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author | Linhui Wang Xuejun Yue Yongxin Liu Jian Wang Huihui Wang |
author_facet | Linhui Wang Xuejun Yue Yongxin Liu Jian Wang Huihui Wang |
author_sort | Linhui Wang |
collection | DOAJ |
description | The rapid development of vision sensor based on artificial intelligence (AI) is reforming industries and making our world smarter. Among these trends, it is of great significance to adapt AI technologies into the intelligent agricultural management. In smart agricultural aviation spraying, the droplets’ distribution and deposition are important indexes for estimating effectiveness in plant protection process. However, conventional approaches are problematic, they lack adaptivity to environmental changes, and consumes non-reusable test materials. One example is that the machine vision algorithms they employ can’t guarantee that the division of adhesive droplets thereby disabling the accurate measurement of critical parameters. To alleviate these problems, we put forward an intelligent visual droplet detection node which can adapt to the environment illumination change. Then, we propose a modified marker controllable watershed segmentation algorithm to segment those adhesive droplets, and calculate their characteristic parameters on the basis of the segmentation results, including number, coverage, coverage density, etc. Finally, we use the intelligent node to detect droplets, and then expound the situation that the droplet region is effectively segmented and marked. The intelligent node has better adaptability and robustness even under the condition of illumination changing. The large-scale distributed detection result indicates that our approach has good consistency with the non-recyclable water-sensitive paper approach. Our approach provides an intelligent and environmental friendly way of tests for spraying techniques, especially for plant protection with Unmanned Aerial Vehicles. |
first_indexed | 2024-04-11T22:18:57Z |
format | Article |
id | doaj.art-35c72a7b663240c597cae713c2217ce0 |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-11T22:18:57Z |
publishDate | 2019-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Sensors |
spelling | doaj.art-35c72a7b663240c597cae713c2217ce02022-12-22T04:00:17ZengMDPI AGSensors1424-82202019-02-0119493310.3390/s19040933s19040933An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition DetectionLinhui Wang0Xuejun Yue1Yongxin Liu2Jian Wang3Huihui Wang4College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, ChinaThe Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USAThe Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USAThe Department of Engineering at Jacksonville University, Jacksonville, FL 32211, USAThe rapid development of vision sensor based on artificial intelligence (AI) is reforming industries and making our world smarter. Among these trends, it is of great significance to adapt AI technologies into the intelligent agricultural management. In smart agricultural aviation spraying, the droplets’ distribution and deposition are important indexes for estimating effectiveness in plant protection process. However, conventional approaches are problematic, they lack adaptivity to environmental changes, and consumes non-reusable test materials. One example is that the machine vision algorithms they employ can’t guarantee that the division of adhesive droplets thereby disabling the accurate measurement of critical parameters. To alleviate these problems, we put forward an intelligent visual droplet detection node which can adapt to the environment illumination change. Then, we propose a modified marker controllable watershed segmentation algorithm to segment those adhesive droplets, and calculate their characteristic parameters on the basis of the segmentation results, including number, coverage, coverage density, etc. Finally, we use the intelligent node to detect droplets, and then expound the situation that the droplet region is effectively segmented and marked. The intelligent node has better adaptability and robustness even under the condition of illumination changing. The large-scale distributed detection result indicates that our approach has good consistency with the non-recyclable water-sensitive paper approach. Our approach provides an intelligent and environmental friendly way of tests for spraying techniques, especially for plant protection with Unmanned Aerial Vehicles.https://www.mdpi.com/1424-8220/19/4/933dropletsintelligent nodevision sensoradaptabilityUnmanned Aerial Vehicles |
spellingShingle | Linhui Wang Xuejun Yue Yongxin Liu Jian Wang Huihui Wang An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection Sensors droplets intelligent node vision sensor adaptability Unmanned Aerial Vehicles |
title | An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection |
title_full | An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection |
title_fullStr | An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection |
title_full_unstemmed | An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection |
title_short | An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection |
title_sort | intelligent vision based sensing approach for spraying droplets deposition detection |
topic | droplets intelligent node vision sensor adaptability Unmanned Aerial Vehicles |
url | https://www.mdpi.com/1424-8220/19/4/933 |
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