Automatic Fruit Quality Inspection System Using Image Processing

Abstract

The demand for high-quality fruits has increased significantly, driven by consumers' growing emphasis on health and nutrition. To ensure consistent quality control and efficient fruit grading processes, an automatic fruit quality inspection system using image processing techniques has been developed. This system leverages Convolutional Neural Network (CNN) algorithms to achieve an impressive accuracy rate of 99% in fruit quality assessment. The proposed system involves a multi-step process starting with the acquisition of high-resolution fruit images. These images undergo pre-processing to enhance clarity and eliminate noise or artifacts. Subsequently, the pre-processed images are fed into a trained CNN model for feature extraction and classification based on learned patterns. The CNN model has been trained on a large dataset of labeled fruit images, enabling it to recognize quality attributes such as colour, size, shape, and defects. The system's evaluation involved a diverse range of fruits, encompassing various species, varieties, and maturity stages. The output demonstrate the system's exceptional accuracy, with a 99% success rate in correctly identifying and grading fruit quality attributes. This automatic fruit quality inspection system offers several advantages, including real-time processing, high efficiency, and reduced labor costs, making it suitable for integration into fruit processing facilities and supply chains. [1]