IECE Transactions on Internet of Things, 2023, Volume 1, Issue 1: 9-14

Research Article | Feature Paper | 12 June 2023
1 School of Informatics, Huazhong Agricultural University, Wuhan 430070, China
2 School of Computer, BaoJi University of Arts and Sciences, Baoji 721016, China
* Corresponding Author
Received: 03 May 2023, Accepted: 09 June 2023, Published: 12 June 2023  

The identification of immature apples is a key technical link to realize automatic real-time monitoring of orchards, expert decision-making, and realization of orchard output prediction. In the orchard scene, the reflection caused by light and the color of immature apples are highly similar to the leaves, especially the obscuration and overlap of fruits by leaves and branches, which brings great challenges to the detection of immature apples. This paper proposes an improved YOLOv3 detection method for immature apples in the orchard scene. Use CSPDarknet53 as the backbone network of the model, introduce the CIOU target frame regression mechanism, and combine with the Mosaic algorithm to improve the detection accuracy. For the data set with severely occluded fruits, the F1 and mAP of the immature apple recognition model proposed in this article are 0.652 and 0.675, respectively. The inference speed for a single 416×416 picture is 12 ms, the detection speed can reach 83 frames/s on 1080ti, and the inference speed is 8.6 ms. Therefore, for the severely occluded immature apple data set, the method proposed in this article has a significant detection effect, and provides a feasible solution for the automation and mechanization of the apple industry.

Graphical Abstract
An Improved YOLOv3-Based Method for Immature Apple Detection

Orchard scene
Immature apple
Improved YOLOv3
Mosaic algorithm
CIOU target frame regression mechanism

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