2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) | 2021
An Automated System for Fruit Gradation and Aberration Localisation using Deep Learning
Abstract
Automated visual inspection using deep learning is widely used in recent years. In the field of agriculture deep learning can be deployed to reduce effective man power, best time utilization and supreme classification with improved accuracy. In agriculture, DL can be imported in many applications like soil identification, disease classification, fruit grading and many more. Fruit quality classification is an essential part in farming as it implies to the return directly. Hence an automated system is much needed to improve the classification of fruits with high accuracy and less time. In this paper a deep neural network CNN is implemented by which the system is able to identify the fruit type and classified weather the fruit is healthy or diseased. This paper also able for aberration localization from the fruit surfaces using R-CNN concept. This work has achieved the optimum result with grading accuracy over 99% and 97.86% using CNN and R-CNN methods respectively.