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Handbooks provide a concise compilation of approved key information on methods of research, general principles, and functional relationships in physical and applied sciences. The world's leading experts in the fields of physics and engineering will be assigned by one or several renowned editors to write the chapters comprising each volume. The content is selected by these experts from Springer sources (books, journals, online content) and other systematic and approved recent publications of scientific and technical information.The volumes are designed to be useful as readable desk reference book to give a fast and comprehensive overview and easy retrieval of essential reliable key information, including tables, graphs, and bibliographies. References to extensive sources are provided.

A machine-vision-system-guided precision sprayer was developed and tested. The longterm objectives of this project were to develop new technologies to estimate weed density and size in real-time, realize site-specific weed control, and effectively reduce herbicide application amounts for corn and soybean fields. This research integrated a real time machine vision sensing system with an automatic herbicide sprayer to create an intelligent sensing and spraying system. Multiple video images were used to cover the target area. To increase the accuracy, each individual spray nozzle was controlled separately. Instead of trying to identify each individual plant in the field, weed infestation zones (0.254 m by 0.34 m) were detected. The integrated system was tested to evaluate the effectiveness and performance under varying field conditions. With the current system design and using 0.5% weed coverage as the control zone threshold, herbicide savings of 48% could be realized.

Finding a pathway between crop rows is essential for automated guidance of some agricultural vehicles. The research reported in this paper developed a vision-based method for detecting crop rows. This method applied the Hough transform and connectivity analysis to process images of a vehicle's forward view and to use them to find the appropriate pathway in the field. The Hough transform was used to detect crop rows and the connectivity analysis was applied to identify the most suitable path from all possible choices. This system was implemented in an agricultural tractor and tested in both laboratory and field experiments. The methodology devised overcame image noise problems and successfully determined the proper trajectory for the tractor.