Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning

主成分分析 模式识别(心理学) 线性判别分析 人工智能 预处理器 数学 偏最小二乘回归 计算机科学 生物系统 统计 生物
作者
Prabahar Ravichandran,Sadhasivam Viswanathan,Sridhar Ravichandran,Ya‐Jun Pan,Young K. Chang
出处
期刊:Cereal chemistry [Wiley]
卷期号:99 (4): 907-919 被引量:7
标识
DOI:10.1002/cche.10546
摘要

Abstract Background and Objectives Grain quality is a complex trait in rice, compared with other staple crops as it is predominantly consumed as a whole grain. Although considered secondary to yield, to align with consumer preferences, breeders are increasingly interested in quality. At the early stages of a breeding program, grain quality‐related traits are often ignored as they are arduous and time‐consuming. Near‐infrared spectroscopy (NIRS) could be a suitable high‐throughput alternative to conventional wet chemistry and image processing‐related methods to be adopted for early screening. This study aims to quantify traits essential for rice breeders such as amylose, chalkiness, length, width, and the length/width ratio in rice samples with NIRS. We used conventional algorithms such as principal component analysis (PCA), partial least square regression (PLSR), multilayer perceptron (MLP), support vector classification (SVC), and linear discriminant analysis (LDA) to compare with the proposed convolutional neural network (CNN) for regression and classification. Findings Our results showed that the proposed CNN outperformed the conventional models in estimating all traits. Unlike conventional models, CNN models could be developed with raw spectra with minimal to no preprocessing, and along with the transfer‐learning capabilities, the time required for model development could be significantly reduced. Conclusion We recommend NIRS for quantitative estimation of amylose and chalkiness in rice and rather use classification/categorized estimation for other physical dimension‐related traits such as length and length/width ratio. Significance and Novelty We found NIRS to be an appropriate alternative to wet chemistry and image‐based methods for screening lines at the early stages of the breeding program. Estimation of physical parameters such as length and length/width ratio with NIRS is novel and appears reasonable for high‐throughput applications.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
白熊发布了新的文献求助30
1秒前
2秒前
2秒前
4秒前
zhu发布了新的文献求助10
5秒前
5秒前
独特听芹完成签到,获得积分10
7秒前
zz完成签到,获得积分10
7秒前
8秒前
晚风完成签到,获得积分10
9秒前
jwj发布了新的文献求助10
10秒前
10秒前
白熊完成签到 ,获得积分10
10秒前
11秒前
李健应助北齐冲浪的鱼采纳,获得10
12秒前
12秒前
王一鸣发布了新的文献求助10
13秒前
ikutovaya完成签到,获得积分10
13秒前
13秒前
奋斗的妙松完成签到,获得积分10
14秒前
老实莫言完成签到,获得积分10
14秒前
15秒前
量子星尘发布了新的文献求助150
15秒前
wop111应助morph采纳,获得20
15秒前
追寻的冬寒完成签到 ,获得积分10
16秒前
17秒前
吼吼吼吼发布了新的文献求助10
17秒前
善学以致用应助生动念烟采纳,获得10
17秒前
由天与发布了新的文献求助10
18秒前
wsy发布了新的文献求助10
19秒前
21秒前
23秒前
23秒前
24秒前
lllllll完成签到,获得积分10
25秒前
25秒前
王一鸣完成签到,获得积分10
26秒前
量子星尘发布了新的文献求助10
28秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
NMR in Plants and Soils: New Developments in Time-domain NMR and Imaging 600
Electrochemistry: Volume 17 600
Physical Chemistry: How Chemistry Works 500
SOLUTIONS Adhesive restoration techniques restorative and integrated surgical procedures 500
Energy-Size Reduction Relationships In Comminution 500
Principles Of Comminution, I-Size Distribution And Surface Calculations 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4950711
求助须知:如何正确求助?哪些是违规求助? 4213460
关于积分的说明 13104286
捐赠科研通 3995337
什么是DOI,文献DOI怎么找? 2186837
邀请新用户注册赠送积分活动 1202090
关于科研通互助平台的介绍 1115359