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Vision Intelligence for Mobile Agro-Robotic System
Noboru Noguchi*, John F. Reid**, Qin Zhang**, Lei Tian** and Al C. Hansen***
*Bioproduction Enginering, School of Agriculture, Graduate School of Hokkaido University, Sapporo, Japan
**Department of Agricultural Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, U.S.A.
***Department of Agricultural Engineering, University of Natal, Scottsville, South Africa
Received:February 1, 1999Accepted:March 8, 1999Published:June 20, 1999
Keywords:fuzzy logic, genetic algorithms, neural networks, machine vision, artificial intelligence, precision farming, geographic information system, global positioning system
Abstract
We developed an intelligent vision system for mobile robot field operations. Fuzzy logic was used to classify crops and weeds. A genetic algorithm (GA) was used to optimize and tune fuzzy logic membership rules. Field studies confirmed that our method accurately classified crops and weeds throughout their growth cycle. After separating out weeds, an artificial neural network (ANN) was used to estimate crop height and width. The r2 for estimating crop height was 0.92 for training data and 0.83 for test data. A geographic information system (GIS) was used to create a crop growth map.
Cite this article as:N. Noguchi, J. Reid, Q. Zhang, L. Tian, and A. Hansen, “Vision Intelligence for Mobile Agro-Robotic System,” J. Robot. Mechatron., Vol.11 No.3, pp. 193-199, 1999.Data files:
Copyright© 1999 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.
