Machine vision-based high-resolution weed mapping and patch-sprayer performance simulation

An experimental machine vision-based patch-sprayer was developed. This sprayer was primarily designed to do real-time weed density estimation and variable herbicide application rate control. However, the sprayer also had the capability to do high-resolution weed mapping if proper mapping techniques were integrated. Two weed mapping methods were developed. One was a GPS signal based off-line weed mapping; another one was radar distance measurement-based on-line weed mapping. The high-resolution weed maps provided evidence to further support the patch-spraying concept. Randomly sampled field images were processed with different nozzle control zone sizes and thresholding methods to simulate sprayer performance. Fundamental system design strategies regarding these two factors were obtained through simulation. System design techniques, including system construction, weed sensing and crop-row detection algorithms were reported

Color image segmentation by genetic algorithm for real time weed sensing

This study was undertaken to develop machine vision-based weed detection technology for outdoor natural lighting conditions. Supervised color image segmentation using a binary-coded genetic algorithm (GA) identifying a region in Hue-Saturation-Intensity (HSI) color space (GAHSI) for outdoor field weed sensing was successfully implemented. Images from two extreme intensity lighting conditions, those under sunny and cloudy sky conditions, were mosaicked to explore the possibility of using GAHSI to locate a plant region in color space when these two extremes were presented simultaneously. The GAHSI result provided evidence for the existence and separability of such a region. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding handsegmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI achieved equivalent performance