Graduate Research in Engineering and Technology (GRET)
Abstract
Fruits are the gift of almighty to nature. Fresh fruit promote good health and having rich source of micronutrients, vitamins and fiber value. But due to its high sugar level on ripping stage different type of pest are attracted by its smell and effects on harvesting. This paper focuses on identification of the pest on ripe fruits using Fuzzy C Means (FCM) clustering for segmentation and simultaneously highlights the segmented insects with Pseudo-coloring using Pseudo-color image processing techniques. IoT integrated Drone based images are inputted as the dataset to perform detection of pest on fruit monitoring system. Before clustering-based segmentation the images undergo preprocessing stage for tone correction and noise removal. Hybrid FCM with Pseudo-color image processing method supersedes many segmentation algorithms by performance.
Recommended Citation
Tungo, Ayush Kumar; Padhan, Rishita; Pradhan, Tanaya Priyadarshini; Dehury, Mandakini; and Jena, Roshan Kumar
(2022)
"Fuzzy C-means Clustering and Pseudo-coloring-based Pest detection of Ripe-Fruit Health Monitoring System using 2-D Aggrotech Images,"
Graduate Research in Engineering and Technology (GRET): Vol. 1:
Iss.
6, Article 9.
DOI: 10.47893/GRET.2022.1138
Available at:
https://www.interscience.in/gret/vol1/iss6/9
DOI
10.47893/GRET.2022.1138
Included in
Agricultural Education Commons, Agricultural Science Commons, Agronomy and Crop Sciences Commons, Apiculture Commons, Biotechnology Commons, Integrative Biology Commons
