Apple Location Method for the Apple Harvesting Robot

Abstract

Abstract-The apple harvesting robot should have two eyes to sensor the location of picking apples. The binocular machine vision system used two Canon digital cameras was built, instead of digital video used as usual. Thus, the digital camera vision system had higher resolution and superior performance than a digital video vision system, which could capture a Jpeg image with 3456*2592 pixels, its field of view included the whole apple tree. For the Fuji apple tree, it is an obvious color difference with ripe apples and their surroundings of leaves and braches. Therefore, the Drg-Drb color index was used to segment apples from their surroundings. Then the mistaken classified background regions was deleted by the area filter, and he picking apples were similarly chosen by area parameter. After that, the conglutinated apples were segmented by the bidirectional scanning line algorithm, which were scanned from the horizontal and vertical direction. Finally, all of picking apples were positioned by their circum-diameter matching algorithm. The experimental result showed that the correct classification rate of picking apple fruit achieved 90%.

A review of key techniques of vision-based control for harvesting robot

“…The authors argued that the total accuracy was 96.36% when the proposed approach was adopted in a greenhouse with artificial illumination. Mao et al (2009) employed a Drg-Drb color indexing to segment apples from background and an accuracy of 90% was achieved. Wei et al (2014) also proposed a fruit recognition method based on an improved OSTU threshold algorithm using a new color feature in the OHTA color space.…”

Using YOLOv3 Algorithm with Pre- and Post-Processing for Apple Detection in Fruit-Harvesting Robot

“…Since color detection depends very much on the lighting conditions, different color spaces other than RGB are usually used: HIS, CIE L*a*b, LCD, and their combinations [7][8][9]. In [10,11], this approach showed a 90% share of correctly recognized apples, and in [12], this approach showed a 95% share of correctly recognized apples, although on very limited datasets (several dozen images).…”

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

“…Due to the rapid development of autonomous robots and precise agriculture, more and more automation equipment is becoming available in the field of agriculture [1,2]. Generally, there are two reasons for this rapid development.…”