Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition

被引:49
作者
Lu, Chuan-Pin [1 ]
Liaw, Jiun-Jian [2 ]
Wu, Tzu-Ching [1 ]
Hung, Tsung-Fu [1 ]
机构
[1] Meiho Univ, Dept Informat Technol, Pingtung 91202, Taiwan
[2] Chaoyang Univ Technol, Dept Informat & Commun Engn, Taichung 41349, Taiwan
来源
AGRONOMY-BASEL | 2019年 / 9卷 / 01期
关键词
deep learning; mushroom cultivation; convolutional neural network; artificial intelligence; computer vision; image measurement;
D O I
10.3390/agronomy9010032
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms.
引用
收藏
页数:21
相关论文
共 50 条
[31]   Deep learning based image recognition for crack and leakage defects of metro shield tunnel [J].
Huang, Hong-wei ;
Li, Qing-tong ;
Zhang, Dong-ming .
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2018, 77 :166-176
[32]   FFT pattern recognition of crystal HRTEM image with deep learning [J].
Zhang, Quan ;
Bai, Ru ;
Peng, Bo ;
Wang, Zhen ;
Liu, Yangyi .
MICRON, 2023, 166
[33]   Beneficiation method of wolframite image recognition based on deep learning [J].
Wang L.-G. ;
Chen S.-J. ;
Jia M.-T. ;
Tu S.-Y. .
Zhongguo Youse Jinshu Xuebao/Chinese Journal of Nonferrous Metals, 2020, 30 (05) :1192-1201
[34]   Bird Image Retrieval and Recognition Using a Deep Learning Platform [J].
Huang, Yo-Ping ;
Basanta, Haobijam .
IEEE ACCESS, 2019, 7 :66980-66989
[35]   Deep Learning Concepts and Datasets for Image Recognition: Overview 2019 [J].
Horak, Karel ;
Sablatnig, Robert .
ELEVENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2019), 2019, 11179
[36]   Applications of Image Recognition Using Deep Learning and Its Challenges [J].
Yamasaki, Kosei ;
Tanaka, Masaharu .
2024 13TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY RESEARCH AND APPLICATIONS, ICRERA 2024, 2024, :1740-1745
[37]   Large Scale Application Response Time Measurement Using Image Recognition and Deep Learning [J].
Vu, Lan ;
Kurkure, Uday ;
Sivarama, Hari ;
Bappanadu, Aravind .
ADVANCES IN VISUAL COMPUTING, ISVC 2018, 2018, 11241 :511-526
[38]   RFID Dynamic Performance Measurement System Embedded in Multiscale Deep Learning [J].
Li, Lin ;
Yu, Xiaolei ;
Liu, Zhenlu ;
Zhao, Zhimin ;
Zhang, Ke ;
Zhou, Shanhao .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[39]   RFID Dynamic Performance Measurement System Embedded in Multiscale Deep Learning [J].
Li, Lin ;
Yu, Xiaolei ;
Liu, Zhenlu ;
Zhao, Zhimin ;
Zhang, Ke ;
Zhou, Shanhao .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[40]   Toward efficient deep learning system for in-situ plankton image recognition [J].
Yue, Junbai ;
Chen, Zhenshuai ;
Long, Yupu ;
Cheng, Kaichang ;
Bi, Hongsheng ;
Cheng, Xuemin .
FRONTIERS IN MARINE SCIENCE, 2023, 10