Mr. Zhao Ming Profile

Zhao Ming

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 19 October 2023 Paper

Algorithm for acquiring lettuce plant height based on image recognition network

Xiyue Guo, Yong Zhong, Ming Zhao, Man Zhang, Minjuan Wang

Proceedings Volume 12709, 127090D (2023) https://doi.org/10.1117/12.2684683

KEYWORDS: Machine learning, Deep learning, Data modeling, Feature extraction, Education and training, RGB color model, Cameras, Random forests, Neural networks, Detection and tracking algorithms

Read Abstract +

Lettuce height is one of the important phenotypic parameters of lettuce. Currently, evaluating plant height information based on RGB images is the most convenient and fastest method. However, these tasks often require complex camera calibration operations or reference objects with known heights, which are somewhat limiting. Therefore, we utilize a deep neural network to solve this problem to directly obtain the height of lettuce from RGB images without camera calibration and reference objects. This paper integrates four high-performance image recognition networks MobileNetV1, Densenet-121, ResNext-50, and EfficientNet-B3 using average fusion and linear fusion methods to predict lettuce plant height. Experiments show that we have achieved good results, the accuracy of the four basic models has been improved, and the average absolute error has been reduced to varying degrees. The smallest reduction is Efficient-Net, which has decreased 0.01 mm, the largest drop is the ResNext-50, which is 0.15 mm lower. After the model is averagely fused, the average absolute error is further reduced to 1.22 mm, and the maximum absolute error is reduced to 6.48 mm, which is not only better than linear. The fusion results are better than the prediction results of a single model.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years