A Statistical Analysis of Fruit and Vegetables Quality Detection and Disease Classification for Smart Farming

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M. Sudhakara, K. Ghamya, Dr. Karthik S A, Dr. G. Yamini, V. Mahalakshmi,


The agricultural industry is the country's primary source of economic growth. Indian agriculture struggles with detecting and identifying fruit and vegetable defects. Fruit and vegetable faults can be spotted using their forms (i.e. colours and textures). Local markets struggle with fruit and vegetable flaws and infections because quality evaluations and classifications are time-consuming. By using the quality of fruit, we can determine how long it will last after purchase. Farmers can predict the best time to harvest fruit to avoid over-ripening. In addition, this will help farmers plan for harvest losses and increase their profits. For defect detection, image processing, machine learning (ML), and artificial intelligence (AI) tools have recently been presented. ML has established itself as cutting-edge technology with multiple applications in various fields. These methods have often been used to judge food quality in recent years. The present state of machine learning methods for estimating food quality and safety is examined in this paper. Product quality is an essential factor in determining the competitiveness of a manufacturing company. First, an introduction to the various approaches to machine learning is presented. Then, a complete comparison of the various methods for identifying the quality of various types of food is presented. To find answers to issues in the food industry, such as identifying the quality of fruits and vegetables, we looked through many research articles. This study found that machine learning techniques in the food industry are superior to more conventional ways.

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