Fuzzy logic inference systems for discriminating plants from soil and residue with machine vision

Timothy W. Hindman; George E. Meyer

doi:10.1117/12.411745

29 December 2000 Fuzzy logic inference systems for discriminating plants from soil and residue with machine vision

Timothy W. Hindman, George E. Meyer

Proceedings Volume 4203, Biological Quality and Precision Agriculture II; (2000) https://doi.org/10.1117/12.411745
Event: Environmental and Industrial Sensing, 2000, Boston, MA, United States

Abstract

This paper summarizes the theory of fuzzy inference systems and its application to plant and weed detection. Two simple examples are presented, both of which discriminate between plants and soil and residue backgrounds in color images based on derived excess green and excess red color indices. The first example shows that a numerical excess red model can be readily replaced with a fuzzy inference system, based on training of red, green, and blue inputs and excess red. The second example shows an arbitrary system of fuzzy inference with excess red and excess green using human preselection, which also gives satisfactory discrimination results.

Citation Download Citation

Timothy W. Hindman and George E. Meyer "Fuzzy logic inference systems for discriminating plants from soil and residue with machine vision", Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); https://doi.org/10.1117/12.411745

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