Apple detection during different growth stages in orchards using the improved YOLO-V3 model

• Published in: Computers and electronics in agriculture Vol. 157; pp. 417 - 426
• Main Authors: Tian, Yunong, Yang, Guodong, Wang, Zhe, Wang, Hao, Li, En, Liang, Zize
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2019; Elsevier BV
• Subjects: Apple images acquisition; Apples; branches; Color; data collection; Deep learning; developmental stages; fruits; Image augmentation; leaves; lighting; Model testing; Occlusion; Orchards; Real time; Real-time detection; Training; Transformations; Variations; YOLOV3-dense; Deep learning; Image augmentation; Real-time detection; Apple images acquisition; YOLOV3-dense

•Proposing an improved YOLO-V3 network processed by DenseNet method.•Realizing the detection of apples in three different growth stages in orchards.•Realizing real-time detection of apples in high resolution images.•Realizing the detection of apples under occlusion and overlap conditions. Real-time...