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

Authors :
Ruoling Deng
Weilin Cheng
Haitao Liu
Donglin Hou
Xiecheng Zhong
Zijian Huang
Bingfeng Xie
Ningxia Yin
Source :
Agriculture, Vol 14, Iss 7, p 1135 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

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.

Details

Language :
English
ISSN :
20770472
Volume :
14
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Agriculture
Publication Type :
Academic Journal
Accession number :
edsdoj.7acd170011c34d9fa6436e455d8e676b
Document Type :
article
Full Text :
https://doi.org/10.3390/agriculture14071135