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Sensors 2018, 18(12), 4245; https://doi.org/10.3390/s18124245

A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops

1
College of Information and Technology, JiLin Agricultural University, Changchun 130118, China
2
Department of Biological Systems Engineering, Centre for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA 99350, USA
*
Authors to whom correspondence should be addressed.
Received: 26 October 2018 / Revised: 22 November 2018 / Accepted: 29 November 2018 / Published: 3 December 2018
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [4013 KB, uploaded 3 December 2018]   |  

Abstract

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications. View Full-Text
Keywords: variable-rate herbicide spraying; weed map; particle swarm optimum algorithm; smart controller variable-rate herbicide spraying; weed map; particle swarm optimum algorithm; smart controller
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, Y.; Gao, Z.; Khot, L.; Meng, X.; Zhang, Q. A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops. Sensors 2018, 18, 4245.

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