Abstract
Variable rate herbicide spraying technology has become integral part of precision agriculture and this system works based on the weed density map of agriculture field. To improve the accuracy of crop/weed discrimination process this paper presents different image processing techniques. Edge detection process for obtaining contour is performed by using sobel operator with 5X5 gradient operator and canny edge detector. Grayscale morphological operations are performed to remove gray overlap due to background of the image in order to improve the accuracy of the segmentation process. In order to check the discrimination accuracy and extracting image features, the experiment was performed on 100 images of maize plant and weed plant leaves. From the experimental results, it is concluded that the proposed method can accurately extract leaf parameters for discrimination process with soil background.
Highlights
Agriculture is the most important part of indian economy, many indians depends on farming for their food and life
Weed plants are the main threat to the crop yield
As can be seen in table I,II and III the algorithm presented in chapter III can extract the useful image features for discrimination process
Summary
Agriculture is the most important part of indian economy, many indians depends on farming for their food and life. The weed plant have narrow or broad leaves and present in inter row or inter column in agriculture field. They are interrupting the growth of the crop by competiting in resource sharing manner like water, sunlight and fertilizer, etc,. This will reduce the quality and quantity of agriculture yield. Traditional practice for weed detection and removal is generally a manual labor method. This method is done by manual removal of weeds, mechanical weeder and using chemical herbicide. In conventional or Revised Manuscript Received on February 15, 2020. * Correspondence Author
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