Intake of healthy fruits and vegetables is vital as they are the source of energy for all living beings. There is an increasing demand for quality in all the consumed food items. Nowadays, starting from consumers, retailers to food manufacturing companies are inspecting food visually for its quality. This manual process incurs more time and it is a laborious and tiring task. So, there is a demand for an automated process which quickly examines, detects the defects and sorts them according to quality. There are many factors such as temperature, humidity etc., affect the quality of fruits. In this work, we have put forward a reliable mechanism for detecting the defects in fruits. The principal goal of this work is to detect and segregate low and best quality fruits. It is achieved using the combination of hardware and image processing techniques and machine learning algorithms. The novelty in this work is interfacing Raspberry Pi with MATLAB and image is captured. The segmentation, feature extraction, and classification is done using MATLAB. Our proposed system exhibits better performance than the existing system.
A. K. N. S. Aishwarya Chandini and B. Uma Maheswari, “Improved Quality Detection Technique for Fruits Using GLCM and MultiClass SVM”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, 2018.