deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 13; p. 4721
• Main Authors: Sa, Inkyu, Lim, Jong Yoon, Ahn, Ho Seok, MacDonald, Bruce
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 22.06.2022; MDPI
• Subjects: Agriculture; Annotations; Color imagery; Conversion; dataset; Datasets; Deep learning; Digital imaging; fruit detection; Fruits; generative adversarial network; Horticulture; Image processing; Infrared imagery; Machine learning; Near infrared radiation; object detection; Satellite imagery; Satellites; Semantics; synthetic infrared image generation; Vegetation

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural netwo...