Automatic Identification of Sea Rice Grains in Complex Field Environment Based on Deep Learning

Ruoling Deng, Weilin Cheng, Haitao Liu, Donglin Hou, Xiecheng Zhong, Zijian Huang, Bingfeng Xie and Ningxia Yin ()
Additional contact information
Ruoling Deng: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Weilin Cheng: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Haitao Liu: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Donglin Hou: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Xiecheng Zhong: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Zijian Huang: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Bingfeng Xie: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
Ningxia Yin: School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China

Agriculture, 2024, vol. 14, issue 7, 1-17

Abstract: The number of grains per sea rice panicle is an important parameter directly related to rice yield, and it is also a very important agronomic trait in research related to sea rice breeding. However, the grain number per sea rice panicle still mainly relies on manual calculation, which has the disadvantages of being time-consuming, error-prone, and labor-intensive. In this study, a novel method was developed for the automatic calculation of the grain number per rice panicle based on a deep convolutional neural network. Firstly, some sea rice panicle images were collected in complex field environment and annotated to establish the sea rice panicle image data set. Then, a sea grain detection model was developed using the Faster R-CNN embedded with a feature pyramid network (FPN) for grain identification and location. Also, ROI Align was used to replace ROI pooling to solve the problem of relatively large deviations in the prediction frame when the model detected small grains. Finally, the mAP (mean Average Precision) and accuracy of the sea grain detection model were 90.1% and 94.9%, demonstrating that the proposed method had high accuracy in identifying and locating sea grains. The sea rice grain detection model can quickly and accurately predict the number of grains per panicle, providing an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research.

Keywords: grain number; rice whole panicle; grain detection; convolutional neural network; plant phenotyping (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/7/1135/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/7/1135/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:7:p:1135-:d:1434356

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().