Pre-Harvest Fruit Image Processing: A Brief Review

Abstract

Agriculture is essential for the development of human civilization. Methods that can precisely estimate the yield of a crop or to perform the harvest automatically using robots can decrease the costs involved and increase production efficiency. With the advancement of agriculture 4.0, current technologies like the internet of things, big data, and artificial intelligence have become more and more common. Systems that use image processing with Deep Learning methods are becoming viable in solving agricultural problems. Deep Learning is part of a large family of methods based on artificial neural networks that can mimic the human brain's work in data processing and pattern recognition for decision-making. Indeed, applications of Deep Learning techniques in agriculture are relatively recent. However, with the rapid advance in Deep Learning and its successful application in agriculture, many articles have been published in recent years. Thus, the main objective of this work was to carry out a brief bibliographic review of pre-harvest fruit image processing techniques, emphasizing the most recent applications using Deep Learning. As seen in the literature, Deep Learning is a promising tool for various agricultural activities, including fruit counting and automatic fruit harvesting using robots.

System of counting green oranges directly from trees using artificial intelligence

“…One of the ways to increase production quality and reduce costs is through technological innovations such as computer vision, which has been showing significant advances in pre-harvest fruit image processing. The two computer vision applications that have recently developed the most in this area were in the fruit production estimation and robotic harvesting sectors as shown by [2].…”

“…Recent advances have been made in this field [2,5]. Nevertheless, there are still many challenges, such as variation in lighting conditions, object occlusion or overlap, low contrast between fruits and foliage, and variation in the shape and pose of the fruits [6,7].…”