A Comprehensive Study on Fruit Classification and Grading Techniques

Traditional methods for fruit grading are based on sensory or psychometric evaluation, which is costly and time-consuming. Different approaches were developed for different fruit like Dates, Apple, Mango, Tomato, and Mulberry based on their features like shape, color, Freshness, acidity, and some other methods also include cellular level analysis and Classification. The most prevalent traits used to detect diseases, ripeness, and fruit class are color, texture, and morphological characteristics. Agriculture automation is used to boost production and quality and promote economic growth. The techniques have evolved to grade fruits in cost-effective, less time-consuming using computer vision and machine learning algorithms that include Artificial Neural Networks and Support Vector Machine. ML techniques, spectrometric sensors, and non-invasive sensing provided better results. In this article, different Machine Learning and Deep Learning methods used along with traditional designs are considered and analyzed. Their mathematical model and algorithms are interpreted, and deep analysis is done.