RESEARCH ON RECOGNITION OF OCCLUDED ORANGE FRUIT ON TREES BASED ON YOLOv4-Reference-Cited by-同舟云学术

RESEARCH ON RECOGNITION OF OCCLUDED ORANGE FRUIT ON TREES BASED ON YOLOv4

Published:2022-08-31 Issue: Volume: Page:137-146
ISSN:2068-2239
Container-title:INMATEH Agricultural Engineering
language:en
Short-container-title:INMATEH

Author:

LI Yan¹,XIAO Liming¹,LI Weiq¹,LI Hao¹,LIU Jie²

Affiliation:

1. College of Engineering, Huazhong Agricultural University, Wuhan, Hubei/ China

2. College of Engineering, Huazhong Agricultural University, Wuhan, Hubei/ China, Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, Hubei/ China, Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan, Hubei/ China

Abstract

For accurate recognition of orange fruit targets, a detection algorithm based on YOLOv4 was applied in this research. The results showed that AP (average precision) of YOLOv4 had reached 98.17%, 2.14% and 2.67% respectively higher than SSD and Faster RCNN while recognition rate of traditional image processing algorithms was merely 54.94%. Additionally, the extent of occlusion was proved to have obvious influences on the accuracy of orange detection. The accuracy on slight occlusion conditions appeared to be higher than that on serious occlusion conditions. Generally, YOLOv4 detection algorithm showed its feasibility and superiority on fruit detection in the complex natural environment.

Publisher

INMA Bucharest-Romania

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science

Reference25 articles.

1. Al-Mallahi, A. (2019). A novel image processing algorithm to separate linearly clustered kiwifruits. Biosystems Engineering, Vol. 183, pp. 184-195, England.

2. Benavides, M., Cantón-Garbín, M., Sánchez-Molina, J., Rodríguez, F. (2020). Automatic Tomato and Peduncle Location System Based on Computer Vision for Use in Robotized Harvesting. Applied Sciences, 10(17): 5887, Switzerland.

3. Cai, S., Sun, Z., Liu, H., Wu, H., Zhuang, Z. (2021). Real-time detection methodology for obstacles in orchards using improved YOLOv4 (基于改进型YOLOv4的果园障碍物实时检测方法). Transactions of the Chinese Society of Agricultural Engineering, Vol. 37 (2), pp: 36-43, Beijing/China.

4. Fountsop, A. N., Fendji, J., Atemkeng, M. (2020). Deep Learning Models Compression for Agricultural Plants. Applied Sciences, 10 (19). 6866, Switzerland.

5. Fu, L., Wang, B., Cui, Y., Su, S., Gejima, Y., Kobayashi, T. (2015). Kiwifruit recognition at nighttime using artificial lighting based on machine vision. International Journal of Agricultural and Biological Engineering, Vol. 4, pp. 52-59, Beijing/ China.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. RESEARCH ON THE RESPONSE MECHANISM OF CLAMPING POINT POSITION TO THE VIBRATION PROPAGATION CHARACTERISTICS OF WOODEN MATERIALS;INMATEH Agricultural Engineering;2024-04-30

2. REASEARCH ON PEAR INFLORESCENCE RECOGNITION BASED ON FUSION ATTENTION MECHANISM WITH YOLOV5;INMATEH Agricultural Engineering;2023-04-30