Deep-learning-based in-field citrus fruit detection and tracking.

ABSTRACT: Fruit yield estimation is crucial to establish fruit harvesting and marketing strategies. Recently, computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited a notable fruit detection ability. However, computer-vision-based citrus fruit counting has two key limitations: inconsistent fruit detection accuracy and double-counting of the same fruit. Using oranges as the experimental material, this paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems. The algorithm consists of two sub-algorithms, OrangeYolo for fruit detection and OrangeSort for fruit tracking. The OrangeYolo backbone network is partially based on the YOLOv3 algorithm and improved upon to detect small object fruit

SUBMITTER: Zhang W 

PROVIDER: S-EPMC9113225 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature