TY - GEN
T1 - A robo-vision algorithm for automatic harvesting of green bell pepper
AU - Moghimi, Ali
AU - Aghkhani, Mohammad Hosein
AU - Golzarian, Mahmood Reza
AU - Rohani, Abbas
AU - Yang, Ce
PY - 2015
Y1 - 2015
N2 - One of the main concerns of greenhouse growers is the cost for labor-intensive tasks including planting, monitoring, spraying and most importantly harvesting. Within the last two decades, there have been great efforts for developing automatic harvesting robots, but they are not commercialized yet. There is a need to conduct further research about different aspects of robots. Machine vision is one major aspect of a harvesting robot, and generally is inseparable part of robot automation. The main objective of this study was to develop a vision system that is simple, low-cost but effective with a reasonable accuracy for detecting bell pepper in greenhouse. Green bell pepper was chosen not only for its nutrient importance but also for its challenging segmentation due to color similarity between samples of interest and leaves. To overcome this challenge, images were firstly segmented into objects. In the next step, texture characteristic as one of the object-based features was utilized to segment objects into smooth and rough classes. Categorized smooth objects were then classified into plant and non-plant regions using adjusted thresholds of color indices of hue, saturation and Excessive Green Index (EGI). This approach produced promising classification results on images taken under natural light for ultimate purpose of automatic harvesting. The algorithm could recognize 94 out of 108 (detection accuracy of 87%) bell peppers located within workspace of robot.
AB - One of the main concerns of greenhouse growers is the cost for labor-intensive tasks including planting, monitoring, spraying and most importantly harvesting. Within the last two decades, there have been great efforts for developing automatic harvesting robots, but they are not commercialized yet. There is a need to conduct further research about different aspects of robots. Machine vision is one major aspect of a harvesting robot, and generally is inseparable part of robot automation. The main objective of this study was to develop a vision system that is simple, low-cost but effective with a reasonable accuracy for detecting bell pepper in greenhouse. Green bell pepper was chosen not only for its nutrient importance but also for its challenging segmentation due to color similarity between samples of interest and leaves. To overcome this challenge, images were firstly segmented into objects. In the next step, texture characteristic as one of the object-based features was utilized to segment objects into smooth and rough classes. Categorized smooth objects were then classified into plant and non-plant regions using adjusted thresholds of color indices of hue, saturation and Excessive Green Index (EGI). This approach produced promising classification results on images taken under natural light for ultimate purpose of automatic harvesting. The algorithm could recognize 94 out of 108 (detection accuracy of 87%) bell peppers located within workspace of robot.
KW - Bell pepper
KW - Computer vision
KW - Edge detection
KW - Harvesting robot
KW - Object-based features
KW - Texture
UR - http://www.scopus.com/inward/record.url?scp=84951949998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951949998&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84951949998
T3 - American Society of Agricultural and Biological Engineers Annual International Meeting 2015
SP - 3185
EP - 3193
BT - American Society of Agricultural and Biological Engineers Annual International Meeting 2015
PB - American Society of Agricultural and Biological Engineers
T2 - American Society of Agricultural and Biological Engineers Annual International Meeting 2015
Y2 - 26 July 2015 through 29 July 2015
ER -