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.
Highlights
In agricultural crop production, indiscriminate application of herbicides leads to environmental pollution and reduced quality of the agricultural products
The variable-rate herbicide spraying (VRHS) system based on prescription chart usually uses sensing in conjunction with Global Positioning System (GPS) and Geographic Information System (GIS) technology for the generation of prescription chart, from which the valuable data are obtained to determine the standard herbicide application rates, but the acquisition of prescription charts is very complex and there may exist large deviations from the real conditions
A VRHS system with DSP controller was developed to control the rate of spraying according to real-time weed distribution mapping
Summary
Indiscriminate application of herbicides leads to environmental pollution and reduced quality of the agricultural products. The VRHS system based on prescription chart usually uses sensing in conjunction with Global Positioning System (GPS) and Geographic Information System (GIS) technology for the generation of prescription chart, from which the valuable data are obtained to determine the standard herbicide application rates, but the acquisition of prescription charts is very complex and there may exist large deviations from the real conditions. Conventional controller used in machine vision based VRHS system has very limited performance, logic operation speed, memory and communication capacity no matter what peripherals are used. This restricts the functionality of the VRHS system
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