The presented work presents a color based segmentation techniques for extraction of yellow rust in whet crop images. Accurate segmentation of yellow rust in wheat crop images is very part of assessment of disease penetration into the wheat crop. And in turn to o take the necessary preventive action for minimizing the crop damage. The jpeg images acquired from CCD camera are read into the matlab tool and a color based segmentation algorithm is performed to segment the yellow rust. The segmentation of color is performed base on k-means algorithm.
The crop of radish is very often infected by a disease that leaves spots of brown, gray or off-white colors on the radish leaves in winter. Scientifically, this disease is known as cercospora leaf spot or cercospora cruciferarum. It's a kind of fungus that often kills young seedlings. The fungus spreads by air and can also infect radish plants when splashed onto leaves during a rainfall as the radish grows up down the soil as stem and leaves come out of the soil. Therefore, it is important to monitor the leaf at regular intervals so as to keep track on quality of growing radish crop. In the presented paper, a novel machine vision system has been proposed that visually inspects the leaves coming out of the soil and based on spots on leaves, it determines the nature of fungus and its depth into the radish stem. The size of the fungus, color depth and location and locus of the fungus on leaves gives an accurate determination of crop quality under the soil. In the presented thesis work, the image of the crop leaves are taken by a good quality color camera and processed for getting a gray colored and segmented image depending upon the nature and size of the fungus. A criterion is set for acceptable and rejects crop quality based on the fungus level.
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