Background of image processing
Image processing techniques have been evolving for years. Image processing and computer vision have been used in different fields and processes for example robots and self-driving cars that use object recognition and edge detection to avoid obstacles. Face recognition systems use computer vision for security. In agriculture, computer vision has been applied for several tasks such as grading, counting and sorting for about two decades. Sorting and grading using computer vision have been improving over the time. It allows farmers to categorize the products accurately and provides better control over their products enabling farmers to make a good decision about the target market.
Sorting and grading of fruits and vegetables have an important role in the post-harvesting process. Manual sorting and grading the products is a very tiring job and requires a lot of time and workers to complete the task. The computer vision techniques, if applied carefully can help the farmer to categorize the fruits and vegetables correctly. Some product counting techniques, able to count the fruits in the image has been proposed and can provide a good estimate of the number of fruits even before harvesting. Automated sorting and grading of vegetables and fruits using computer vision are accomplished using the digital photograph of the products. The automated sorting and grading use non-destructive visual features to classify the products, meaning the product can be classified quite accurately without damaging it. Visual fruits and vegetables grading consists of six major steps:
Acquisition of digital image.
Removal of background.
Calculation of size.
Determining the ripeness.
Detection of surface defects.
Machine learning techniques to predict the quality.