Fruit Recognition on Trees Using Artificial Intelligence

Today, in the industrial and agricultural sectors, one of the most important needs is system automation and saving time and costs. In agriculture, mechanizing processes such as harvesting and categorizing products can play a very effective role in reducing expenses, decreasing the reliance on manual labor, and increasing productivity. By utilizing modern technologies like artificial intelligence and image processing, the automatic and precise identification and classification of fruits on the tree have been made possible. In this context, one of the main challenges is distinguishing between the fruit and other parts of the tree, such as branches and leaves, in the image. One effective method in this field is the K-means clustering algorithm, which, based on features extracted from images, can recognize and separate the fruits from other parts of the tree. Ultimately, this leads to significant savings in time, costs, and labor. In this paper, by implementing the K-means clustering algorithm on a dataset of tree images, it was demonstrated that this method can accurately and effectively identify and separate the fruits from other parts of the tree, such as branches and leaves. The results show that this system has high potential to improve automated harvesting and product classification processes in agriculture.

کلیدواژه ها:

Artificial intelligence (AI) ، Image processing ، K-means