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

A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to adapt the YOLOv3 algorithm to...

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Veröffentlicht in:	Agronomy (Basel) 2020-07, Vol.10 (7), p.1016, Article 1016
Hauptverfasser:	Kuznetsova, Anna, Maleva, Tatiana, Soloviev, Vladimir
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Agriculture Agronomy Algorithms Apples Automation Citrus fruits Color Error detection fruit harvesting robot fruit yield counting robot Fruits Harvest Horticulture Life Sciences & Biomedicine Lighting Machine learning Machine vision Object recognition Orchards Plant Sciences Post-production processing Productivity Prototypes Quality Research methodology Robots Science & Technology Vision systems YOLO
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Zusammenfassung:	A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to adapt the YOLOv3 algorithm to be used in an apple-harvesting robot machine vision system, providing an average apple detection time of 19 ms with a share of objects being mistaken for apples at 7.8% and a share of unrecognized apples at 9.2%. Both the average detection time and error rates are less than in all known similar systems. The system can operate not only in apple-harvesting robots but also in orange-harvesting robots.
ISSN:	2073-4395 2073-4395
DOI:	10.3390/agronomy10071016