Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5

Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved YOLOv5 fruit detection network and a yield fi...

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Autores principales:	LI Zhijun, YANG Shenghui, SHI Deshuai, LIU Xingxing, ZHENG Yongjun
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	apple in-situ yield estimation deep learning fruit detection bp neural network yolov5 Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/4330a6e51bc34c0dae64fa072eb0b217
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spelling	oai:doaj.org-article:4330a6e51bc34c0dae64fa072eb0b2172021-11-17T07:52:11ZYield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv52096-809410.12133/j.smartag.2021.3.2.202105-SA005https://doaj.org/article/4330a6e51bc34c0dae64fa072eb0b2172021-06-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-2-100.shtmlhttps://doaj.org/toc/2096-8094Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved YOLOv5 fruit detection network and a yield fitting network was proposed. The in-situ images of the apples without bags at different periods were acquired by using an unmanned aerial vehicle and Raspberry Pi camera, formed an image sample data set. For dealing with no attention preference and the parameter redundancy in feature extraction, the YOLOv5 network was improved by two approaches: 1) replacing the depth separable convolution, and 2) adding the attention mechanism module, so that the computation cost was decreased. Based on the improvement, the quantity of fruit was estimated and the total area of the bounding box of apples were respectively obtained as output. Then, these results were used as the input of the yield fitting network and actual yields were applied as the output to train the yield fitting network. The final model of fruit tree production estimation was obtained by combining the improved YOLOv5 network and the yield fitting network. Yield estimation experimental results showed that the improved YOLOv5 fruit detection algorithm could improve the recognition accuracy and the degree of lightweight. Compared with the previous algorithm, the detection speed of the algorithm proposed in this research was increased by up to 15.37%, while the mean of average accuracy (mAP) was raised up to 96.79%. The test results based on different data sets showed that the lighting conditions, coloring time and with white cloth in background had a certain impact on the accuracy of the algorithm. In addition, the yield fitting network performed better on predicting the yield of apple trees. The coefficients of determination in the training set and test set were respectively 0.7967 and 0.7982. The prediction accuracy of different yield samples was generally stable. Meanwhile, in terms of the with/without of white cloth in background, the range of relative error of the fruit tree yield measurement model was respectively within 7% and 13%. The yield estimation method of apple tree based on improved lightweight YOLOv5 had good accuracy and effectiveness, which could achieve yield estimation of apples in the natural environment, and would provide a technical reference for intelligent agricultural equipment in modern orchard environment.LI ZhijunYANG ShenghuiSHI DeshuaiLIU XingxingZHENG YongjunEditorial Office of Smart Agriculturearticleapple in-situ yield estimationdeep learningfruit detectionbp neural networkyolov5Agriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 2, Pp 100-114 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	apple in-situ yield estimation deep learning fruit detection bp neural network yolov5 Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	apple in-situ yield estimation deep learning fruit detection bp neural network yolov5 Agriculture (General) S1-972 Technology (General) T1-995 LI Zhijun YANG Shenghui SHI Deshuai LIU Xingxing ZHENG Yongjun Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5
description	Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved YOLOv5 fruit detection network and a yield fitting network was proposed. The in-situ images of the apples without bags at different periods were acquired by using an unmanned aerial vehicle and Raspberry Pi camera, formed an image sample data set. For dealing with no attention preference and the parameter redundancy in feature extraction, the YOLOv5 network was improved by two approaches: 1) replacing the depth separable convolution, and 2) adding the attention mechanism module, so that the computation cost was decreased. Based on the improvement, the quantity of fruit was estimated and the total area of the bounding box of apples were respectively obtained as output. Then, these results were used as the input of the yield fitting network and actual yields were applied as the output to train the yield fitting network. The final model of fruit tree production estimation was obtained by combining the improved YOLOv5 network and the yield fitting network. Yield estimation experimental results showed that the improved YOLOv5 fruit detection algorithm could improve the recognition accuracy and the degree of lightweight. Compared with the previous algorithm, the detection speed of the algorithm proposed in this research was increased by up to 15.37%, while the mean of average accuracy (mAP) was raised up to 96.79%. The test results based on different data sets showed that the lighting conditions, coloring time and with white cloth in background had a certain impact on the accuracy of the algorithm. In addition, the yield fitting network performed better on predicting the yield of apple trees. The coefficients of determination in the training set and test set were respectively 0.7967 and 0.7982. The prediction accuracy of different yield samples was generally stable. Meanwhile, in terms of the with/without of white cloth in background, the range of relative error of the fruit tree yield measurement model was respectively within 7% and 13%. The yield estimation method of apple tree based on improved lightweight YOLOv5 had good accuracy and effectiveness, which could achieve yield estimation of apples in the natural environment, and would provide a technical reference for intelligent agricultural equipment in modern orchard environment.
format	article
author	LI Zhijun YANG Shenghui SHI Deshuai LIU Xingxing ZHENG Yongjun
author_facet	LI Zhijun YANG Shenghui SHI Deshuai LIU Xingxing ZHENG Yongjun
author_sort	LI Zhijun
title	Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5
title_short	Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5
title_full	Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5
title_fullStr	Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5
title_full_unstemmed	Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5
title_sort	yield estimation method of apple tree based on improved lightweight yolov5
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/4330a6e51bc34c0dae64fa072eb0b217
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_version_	1718425855099666432

Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5

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