Multiple Fruit Disease Detection using Deep Learning

Author(s)	Kshitij Patil, Varadraj Jadhav, Harshal Giri, Harish Thore, Hemant Pawar, Dr. Tushar Phadtare
Country	India
Abstract	We propose a computer vision system that detects the external defects of fruits using multi-spectral imaging sensor. First, the pro- posed algorithm segments the fruit from the captured Near-Infra Red (NIR) and RGB images using only the NIR component. Second, some adaptive pre-processing techniques are applied on the segmented RGB and NIR fruit images. Hence, a thresholding technique is utilized in order to detect the defects in seven different color components of the fruit. Finally, a voting process is applied on the seven threshold color components images to determine if the fruit image is defected or defect free.
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-29
Cite This	Multiple Fruit Disease Detection using Deep Learning - Kshitij Patil, Varadraj Jadhav, Harshal Giri, Harish Thore, Hemant Pawar, Dr. Tushar Phadtare - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21378
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.21378
Short DOI	https://doi.org/gtw6r6

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