Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming

Yi Tian; Ye Huang; Yi Chen

doi:10.1007/978-981-99-8498-5_12

Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming

Yi Tian, Ye Huang, Yi Chen^*

^*Corresponding author for this work

School of Intelligent Manufacturing Ecosystem

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

In recent years, autonomous pollination has become a prevalent topic since it can be an excellent method to mitigate the influence of biological and labour-related variables on the pollination process. This study mainly focuses on the flower detection part of the process (using the kiwi flower as a prototype), which involves flower recognition and gender recognition (distinguishing the stamen and pistil). The present study utilized the YOLOv5 model for object detection. Additionally, three CNN models, namely LeNet, AlexNet, and ResNet, were trained to recognize the gender of the kiwi flowers. The performance of these models was evaluated and compared. Upon careful analysis, it was evident that the LeNet model was most effective in identifying the gender of kiwi flowers, with a test set accuracy of 91%. This result suggests that the trained LeNet model can perform better to accurately recognize the gender of kiwi flowers.

Original language	English
Title of host publication	Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
Editors	Andrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	153-168
Number of pages	16
ISBN (Print)	9789819984978
DOIs	https://doi.org/10.1007/978-981-99-8498-5_12
Publication status	Published - 2024
Event	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China Duration: 22 Aug 2023 → 23 Aug 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	845
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Country/Territory	China
City	Suzhou
Period	22/08/23 → 23/08/23

Keywords

AlexNet
Flower detection
Flower gender recognition
LeNet
ResNet
YOLOv5

Access to Document

10.1007/978-981-99-8498-5_12

Cite this

Tian, Y., Huang, Y., & Chen, Y. (2024). Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming. In A. Tan, F. Zhu, H. Jiang, K. Mostafa, E. H. Yap, L. Chen, L. J. A. Olule, & H. Myung (Eds.), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023 (pp. 153-168). (Lecture Notes in Networks and Systems; Vol. 845). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8498-5_12

Tian, Yi ; Huang, Ye ; Chen, Yi. / Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming. Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. editor / Andrew Tan ; Fan Zhu ; Haochuan Jiang ; Kazi Mostafa ; Eng Hwa Yap ; Leo Chen ; Lillian J. A. Olule ; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. pp. 153-168 (Lecture Notes in Networks and Systems).

@inproceedings{9d885d288a1a4705bde2117c3898ef00,

title = "Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming",

abstract = "In recent years, autonomous pollination has become a prevalent topic since it can be an excellent method to mitigate the influence of biological and labour-related variables on the pollination process. This study mainly focuses on the flower detection part of the process (using the kiwi flower as a prototype), which involves flower recognition and gender recognition (distinguishing the stamen and pistil). The present study utilized the YOLOv5 model for object detection. Additionally, three CNN models, namely LeNet, AlexNet, and ResNet, were trained to recognize the gender of the kiwi flowers. The performance of these models was evaluated and compared. Upon careful analysis, it was evident that the LeNet model was most effective in identifying the gender of kiwi flowers, with a test set accuracy of 91%. This result suggests that the trained LeNet model can perform better to accurately recognize the gender of kiwi flowers.",

keywords = "AlexNet, Flower detection, Flower gender recognition, LeNet, ResNet, YOLOv5",

author = "Yi Tian and Ye Huang and Yi Chen",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 ; Conference date: 22-08-2023 Through 23-08-2023",

year = "2024",

doi = "10.1007/978-981-99-8498-5_12",

language = "English",

isbn = "9789819984978",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "153--168",

editor = "Andrew Tan and Fan Zhu and Haochuan Jiang and Kazi Mostafa and Yap, {Eng Hwa} and Leo Chen and Olule, {Lillian J. A.} and Hyun Myung",

booktitle = "Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023",

}

Tian, Y, Huang, Y & Chen, Y 2024, Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming. in A Tan, F Zhu, H Jiang, K Mostafa, EH Yap, L Chen, LJA Olule & H Myung (eds), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Lecture Notes in Networks and Systems, vol. 845, Springer Science and Business Media Deutschland GmbH, pp. 153-168, International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023, Suzhou, China, 22/08/23. https://doi.org/10.1007/978-981-99-8498-5_12

Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming. / Tian, Yi; Huang, Ye; Chen, Yi.
Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. ed. / Andrew Tan; Fan Zhu; Haochuan Jiang; Kazi Mostafa; Eng Hwa Yap; Leo Chen; Lillian J. A. Olule; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. p. 153-168 (Lecture Notes in Networks and Systems; Vol. 845).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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T1 - Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming

AU - Tian, Yi

AU - Huang, Ye

AU - Chen, Yi

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - In recent years, autonomous pollination has become a prevalent topic since it can be an excellent method to mitigate the influence of biological and labour-related variables on the pollination process. This study mainly focuses on the flower detection part of the process (using the kiwi flower as a prototype), which involves flower recognition and gender recognition (distinguishing the stamen and pistil). The present study utilized the YOLOv5 model for object detection. Additionally, three CNN models, namely LeNet, AlexNet, and ResNet, were trained to recognize the gender of the kiwi flowers. The performance of these models was evaluated and compared. Upon careful analysis, it was evident that the LeNet model was most effective in identifying the gender of kiwi flowers, with a test set accuracy of 91%. This result suggests that the trained LeNet model can perform better to accurately recognize the gender of kiwi flowers.

AB - In recent years, autonomous pollination has become a prevalent topic since it can be an excellent method to mitigate the influence of biological and labour-related variables on the pollination process. This study mainly focuses on the flower detection part of the process (using the kiwi flower as a prototype), which involves flower recognition and gender recognition (distinguishing the stamen and pistil). The present study utilized the YOLOv5 model for object detection. Additionally, three CNN models, namely LeNet, AlexNet, and ResNet, were trained to recognize the gender of the kiwi flowers. The performance of these models was evaluated and compared. Upon careful analysis, it was evident that the LeNet model was most effective in identifying the gender of kiwi flowers, with a test set accuracy of 91%. This result suggests that the trained LeNet model can perform better to accurately recognize the gender of kiwi flowers.

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BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023

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A2 - Yap, Eng Hwa

A2 - Chen, Leo

A2 - Olule, Lillian J. A.

A2 - Myung, Hyun

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

Y2 - 22 August 2023 through 23 August 2023

ER -

Tian Y, Huang Y, Chen Y. Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming. In Tan A, Zhu F, Jiang H, Mostafa K, Yap EH, Chen L, Olule LJA, Myung H, editors, Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 153-168. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-8498-5_12

Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming

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