Abstract
This paper deals with an enhanced computer vision-based algorithm for counting the number of apple fruits in an individual tree. This paper, examine six diverse methodologies to denoising the input image and compare the statistical evaluation of these methods. In this correlation, the wavelet-based image denoising strategy performed well. This filter output image is very suitable for the next stage of image segmentation because it improves the fruit recognition rate. Analysis shows that this proposed algorithm has the accuracy of 93% and an overall error rate of 1.5%. These results show that this fruit counting algorithm is suitable for real-time application such as automatic fruit yield estimation.
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Thendral, R., David, D.S. (2022). An Enhanced Computer Vision Algorithm for Apple Fruit Yield Estimation in an Orchard. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_27
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DOI: https://doi.org/10.1007/978-981-16-6448-9_27
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