• Citations Per Year
Learn More
A texture–based weed classification method was developed. The method consisted of a low–level Gabor wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories(More)
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(More)
A novel texture-based weed classification method was developed. The method comprised a low-level Gabor wavelets-based feature extraction algorithm and a high-level neural network-based pattern recognition algorithm. The design strategy simulated the function of the human visual system, which uses low-level receptors for early stage vision processing and(More)
For effective operation of a selective sprayer with real–time local weed sensing, herbicides must be delivered accurately to weed targets in the field. With a machine vision–based selective spraying system, acquiring sequential images and switching nozzles on and off at the correct locations are critical. An MS Windows–based imaging system was interfaced(More)
Segmentation of vegetation is a critical step in using machine vision for field automation tasks. A new method called reduced−dimension clustering (RDC) was developed based on theoretical considerations about the color distribution of field images. RDC performed unsupervised classification of pixels in field images into vegetation and background classes.(More)
Image processing algorithms for individual corn plant and plant stem center identification were developed. These algorithms were applied to mosaicked crop row image for automatically measuring corn plant spacing at early growth stages. These algorithms utilized multiple sources of information for corn plant detection and plant center location estimation(More)
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 being integrated. Two weed mapping methods were developed.(More)
Topographic data collected using RTK‐DGPS‐equipped farm vehicles during field operations could add additional benefits to the original capital investment in the equipment through the development of high‐accuracy field DEMs. Repeated surveys of elevation data from field operations may improve DEM accuracy over time. However, minimizing the amount of data to(More)
The cirrus clouds are a sort of transparent clouds that are barely visible in many satellite images. These clouds form a reflection effect in the images which hide the crucial information in remote sensing. Thus the removal of cirrus effect is essential to have an effective remote sensing over coastal regions and the following proposed algorithms proved to(More)
Loss of function of mutated solute carrier family 12 member 3 (SLC12A3) gene is the most frequent etiology for Gitelman syndrome (GS), which is mainly manifested by hypokalemia, hypomagnesemia and hypocalciuria. We report the genetic characteristics of one suspicious Chinese GS pedigree by gene sequencing. Complete sequencing analysis of the SLC12A3 gene(More)