A comprehensive review of machine vision systems and artificial intelligence algorithms for the detection and harvesting of agricultural produce

Abstract

Every nation's economic development depends heavily on agriculture. Fulfilling the current population's need for food is becoming increasingly difficult because of factors including population growth, frequent climate change, and a lack of resources. However, the agriculture sector's biggest problems are a lack of trained workers, urbanization, and a lack of available labour. Automation in agriculture is essential to provide food, fibre, and fuels to the rapidly growing population. Since harvesting is a critical step in farming, the authors present a systematic review of machine vision systems and artificial intelligence algorithms for detecting and harvesting agricultural produce in this article. The areas that are being concentrated on include machine vision systems, vision sensors, and different image processing and artificial intelligence algorithms utilized for detection and harvesting. Review of various image types and vision sensors used in machine vision systems for automated detection and harvesting. It demonstrates how several 3D methods, which were used to obtain the position, orientation, and 3D point cloud of the fruit or crop, function and compare them. Furthermore, it compares various image processing and artificial intelligence algorithms deployed in precision agriculture for detection and harvesting. This article shows how knowledge-based agriculture can boost agriculture produce and quality.