A new Expert System for greenness identification in agricultural images
It is well-known that one important issue emerging strongly in agriculture is related with the automation of tasks, where camera-based sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they comes from barley and maize fields including weeds, so that some type of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. The images come from outdoor environments, which are affected for a high variability of illumination conditions because of sunny or cloudy days or both with high rate of changes. Several indices have been proposed in the literature for greenness identification, but under adverse environmental conditions most of them fail or do not work properly. This is true even for camera devices with auto-image white balance. This paper proposes a new automatic and robust Expert System for greenness identification. It consists of two main modules: (1) decision making, based on image histogram analysis and (2) greenness identification, where two different strategies are proposed, the first based on classical greenness identification methods and the second inspired on the Fuzzy Clustering approach. The Expert System design as a whole makes a contribution, but the Fuzzy Clustering strategy makes the main finding of this paper. The system is tested for different images captured with several camera devices.
expert system image processing fuzzy clustering machine vision automatic greenness identification