Crop Sorting Using Image Segmentation and Artificial Intelligence: A Review

As a consequence of the global increment in population, it becomes required to provide them with varieties of high-quality agricultural crops in huge quantities, which would be problematic without mechanization, thus, it is necessary to employ artificial intelligence and computer vision in the process of sorting and classifying these crops. Recently, many Technologies in the automatic sorting machine identifying most types of crops and sorting them from good to bad. The speed and accuracy of these technologies will increase the mass production and quality requirements and helps to minimize the overwork on human labor by saving cost and time. This paper has reviewed different methods used to sort and classify agricultural crops and fruits using image processing, the segmentation process is one of the most important techniques in image processing to simplify and classify the image to be able to pre-process a specific part of it by using different methods. Previous work was reviewed where the thresholding method was used to simplify the image processing by eliminating the background. The image was classified using k-mean clustering which helped in detecting the defected areas and other methods. The accuracy of the segmentation methods ranged between 80 to 99%, depending on the method used and its purpose which is a good percentage. The main goal of this paper is to introduces the information on image segmentation method, its types, and the most popular methods have been used in agriculture, by takes the advantage of Artificial intelligent.