Application of computer vision and color image segmentation for yield prediction precision

Abstract

Precision agriculture is finding its roots in India. PA always deals with the accuracy and timely information about agriculture products. With the rapid development of computer hardware and software technology, the application of image processing technology in the agricultural research are playing key role [1]. Also, with the advantages of superior speed and accuracy, computer vision has attracted it as an alternative to human inspection [2]. In this paper, we have described a novel application of computer vision and color image segmentation for automating the precise yield prediction process of gerbera flower yield from the polyhouse images. The purpose of the present study is to design a decision support system that could generate flower yield information and serve as base for management & planning of flower marketing. Current study has applied the color image segmentation technique using threshold, to extract the flowers from the scene. Color is considered a fundamental physical property of agriculture products and foods in information analysis [3]. Using HSV color space and histogram analysis, flower color definition is done. Then by the image segmentation process, flowers were separated from the background & detected in the images. Image set with 75 images were tested with this technique.

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Cite this paper

@article{Sarkate2013ApplicationOC, title={Application of computer vision and color image segmentation for yield prediction precision}, author={Rajesh S. Sarkate and Namdeo V. Kalyankar and Prakash B. Khanale}, journal={2013 International Conference on Information Systems and Computer Networks}, year={2013}, pages={9-13} }