Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placin...

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Autores principales:	Lifa Fang, Yanqiang Wu, Yuhua Li, Hongen Guo, Hua Zhang, Xiaoyu Wang, Rui Xi, Jialin Hou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image recognition deep learning YOLO attention mechanism MobileNetv2 ginger Agriculture S
Acceso en línea:	https://doaj.org/article/7c816dfc4f1549ea8abefe849e1a9e46
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spelling	oai:doaj.org-article:7c816dfc4f1549ea8abefe849e1a9e462021-11-25T16:11:41ZGinger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network10.3390/agronomy111123282073-4395https://doaj.org/article/7c816dfc4f1549ea8abefe849e1a9e462021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2328https://doaj.org/toc/2073-4395A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.Lifa FangYanqiang WuYuhua LiHongen GuoHua ZhangXiaoyu WangRui XiJialin HouMDPI AGarticleimage recognitiondeep learningYOLOattention mechanismMobileNetv2gingerAgricultureSENAgronomy, Vol 11, Iss 2328, p 2328 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image recognition deep learning YOLO attention mechanism MobileNetv2 ginger Agriculture S
spellingShingle	image recognition deep learning YOLO attention mechanism MobileNetv2 ginger Agriculture S Lifa Fang Yanqiang Wu Yuhua Li Hongen Guo Hua Zhang Xiaoyu Wang Rui Xi Jialin Hou Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network
description	A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.
format	article
author	Lifa Fang Yanqiang Wu Yuhua Li Hongen Guo Hua Zhang Xiaoyu Wang Rui Xi Jialin Hou
author_facet	Lifa Fang Yanqiang Wu Yuhua Li Hongen Guo Hua Zhang Xiaoyu Wang Rui Xi Jialin Hou
author_sort	Lifa Fang
title	Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network
title_short	Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network
title_full	Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network
title_fullStr	Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network
title_full_unstemmed	Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network
title_sort	ginger seeding detection and shoot orientation discrimination using an improved yolov4-lite network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7c816dfc4f1549ea8abefe849e1a9e46
work_keys_str_mv	AT lifafang gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT yanqiangwu gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT yuhuali gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT hongenguo gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT huazhang gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT xiaoyuwang gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT ruixi gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork AT jialinhou gingerseedingdetectionandshootorientationdiscriminationusinganimprovedyolov4litenetwork
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Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

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