Moreover, as the detection algorithms are complicated and have many fixed thresholds, it is difficult to adapt them to other fruits and/or environments. However, the detection accuracy of such methods is heavily dependent on the illumination conditions. Conventional techniques rely mainly on color, texture, shape, and other shallow features of the image for detection. Many techniques have been developed for fruit detection over the last decade. Of the two steps, fruit detection is the most crucial, since it is vital that only the fruit which are ripe and ready for consumption are harvested, while the remainder are left on the branch or vine to mature. Robotic harvesting comprises two main steps: fruit detection using a computer vision system and fruit picking using a robot arm. However, with the development of artificial intelligence (AI), much of this work can now be performed by robots. Fruit harvesting is labor-intensive and time-consuming work.
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