YOLOv5 Deep Neural Network for Quince and Raspberry Detection on RGB Images

• Published in: 2022 Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW) pp. 19 - 22
• Main Authors: Sudars, Kaspars, Namatevs, Ivars, Judvaitis, Janis, Balass, Rihards, Nikulins, Arturs, Peter, Astile, Strautina, Sarmite, Kaufmane, Edite, Kalnina, Ieva
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.10.2022
• Subjects: Deep learning; Deep neural networks; Image processing; Neural networks; Point cloud compression; Precise agriculture; Three-dimensional displays; Training; Wireless communication; Yield estimation; YOLOv5
• DOI: 10.1109/MTTW56973.2022.9942550

Object detection based on deep learning can be widely used in all kinds of agricultural applications. In this paper, we present a deep neural network (DNN) model for quince and raspberry detection on RGB images. The trained DNN model is based on YOLOv5 architecture and it has 7 berry classes related to the berry development stage. YOLOv5 provides sufficiently good performance and precision trade-off. It is useful in the process of quince and raspberry phenotyping for the agriculture experts, where the yield and berry size parameters have to be estimated. Using our DNN model we have shown that it is possible to achieve a mean Average Precision close to 80.9 % and in some cases (Average Precision) close to 95 % for some classes. The DNN model is trained on labeled data gathered during the AKFEN project. The developed raspberry and quince detector is freely available at the GIT repository [1]. Further, the research on Sensor Networks, Wireless Systems, 3D point cloud processing and multi-spectral image processing has to be carried out leading to high-throughput phenotyping.