Vol. 12, Issue 6 (2023)

Author(s):
Sridevy Sridarane, Kumaresan Palaniappan, M Nirmala Devi and M Djanaguiraman

Abstract:
Tomato is well known fruit since it has many essential and beneficial nutrients like antioxidant, vitamin C and A for human daily diet. Tomato picking by hand is both labor and time consuming. Therefore, to overcome these issues, tomato needs to be picked up automatically with the help of harvesting robot. Recently automation of fruit harvesting gains great popularity. To guide the harvesting robot to pick up the fruit correctly, it is important to correctly detect and find the location of the red mature fruit. A computer vision approach is proposed to detect the fruit by capturing the tomato images and classify. A deep learning classifier is utilized to make a robust decision that covers a wide variety of tomato apperance. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato. The model is tested on 2000 images which is prepared by us. The results show that MobileNet V2 is able to detect & Classify up to more than 95% accuracy.