Ashwani Kumar Aggarwal, Preeti Jaidka



Segmentation of Crop Images for Crop Yield Prediction

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The crop yield prediction is very useful for the farmers to manage the farming practices. The prediction accuracy depends on various pre-processing tasks including but not limited to segmentation of crop images. There are several methods for segmentation of crop images but each of these methods suffer from one drawback or the other. In this paper, a segmentation based on k-means is presented. Prior to the k-means clustering, the input image is filtered using box blur kernel. The filtered image is histogram equalized using histogram equalization method. The method is tested on a huge dataset. The accuracy of the method is calculated, and it has been observed that the method outperforms existing methods and is computationally inexpensive.


Crop yield prediction, segmentation, pre-processing, thresholding, image processing


Cite this paper

Ashwani Kumar Aggarwal, Preeti Jaidka. (2022) Segmentation of Crop Images for Crop Yield Prediction. International Journal of Biology and Biomedicine, 7, 40-44


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