Automated or robot-assisted collection is an evolving research domain that mixes aspects of machine vision and machine intelligence. When combined with robotics, image processing has proven to be an efficient method for analysis in various performance areas, namely agricultural applications. Most of it had been applied to the robot, which may want to pick fruit and type various fruits and vegetables. Identification and classification could even be a serious obstacle to computer vision demanding near-human levels of recognition. The target of this survey is to classify and briefly review the literature on harvesting robots that use different techniques and computer analysis of images of fruits and vegetables in agricultural activities, which incorporates 25 articles published within the last three decades. The proposed approach takes under consideration various sorts of fruit. Much research on this subject has been conducted in recent years, either implementing simple techniques such as computer vision like color-based clustering or using other sensors like LWIR, hyperspectral, or 3D. Current advances in computer vision offer an honest sort of advanced object detection techniques that would dramatically increase the quality of efficiency of fruit detection from RGB images. Some performance evaluation metrics obtained in various experiments are emphasized for the revised techniques, thus helping researchers to settle on and make new computer vision applications in fruit images.
Cooperation of unmanned systems for agricultural applications: A case study in a vineyard
