Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network

高光谱成像 卷积神经网络 支持向量机 人工智能 模式识别(心理学) 随机森林 计算机科学 规范化(社会学) 预处理器 数学 人类学 社会学
作者
Ke-Jun Fan,Boyuan Liu,Wen‐Hao Su
出处
期刊:Sensors [Multidisciplinary Digital Publishing Institute]
卷期号:23 (5): 2668-2668
标识
DOI:10.3390/s23052668
摘要

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382–1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
清脆初柳发布了新的文献求助10
刚刚
刚刚
1秒前
XXXX完成签到 ,获得积分10
2秒前
wbb发布了新的文献求助10
3秒前
刻苦颤完成签到,获得积分20
4秒前
4秒前
5秒前
5秒前
充电宝应助勤劳不弱采纳,获得10
7秒前
文某关注了科研通微信公众号
8秒前
体贴寒烟发布了新的文献求助20
8秒前
9秒前
9秒前
乐观的大白菜真实的钥匙完成签到,获得积分10
10秒前
路人甲完成签到,获得积分10
10秒前
脑洞疼应助听忆采纳,获得10
11秒前
邬佳仁发布了新的文献求助10
11秒前
orixero应助谦让的心锁采纳,获得10
13秒前
乐空思应助愿好采纳,获得30
13秒前
13秒前
Flipped发布了新的文献求助10
14秒前
14秒前
小久笑完成签到,获得积分10
14秒前
PeterLin完成签到,获得积分10
15秒前
15秒前
Chaming完成签到,获得积分20
16秒前
lyk2815完成签到,获得积分10
18秒前
12完成签到 ,获得积分10
18秒前
PeterLin发布了新的文献求助10
19秒前
无极微光应助吴祥坤采纳,获得20
19秒前
yyy完成签到,获得积分10
20秒前
20秒前
Chaming发布了新的文献求助10
20秒前
21秒前
kiyo_v发布了新的文献求助10
22秒前
cg666完成签到 ,获得积分10
23秒前
kk发布了新的文献求助10
23秒前
25秒前
pp7完成签到,获得积分10
27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
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1500
Cowries - A Guide to the Gastropod Family Cypraeidae 1200
Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development 800
Pulse width control of a 3-phase inverter with non sinusoidal phase voltages 777
Signals, Systems, and Signal Processing 610
Research Methods for Applied Linguistics: A Practical Guide 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6401049
求助须知:如何正确求助?哪些是违规求助? 8218025
关于积分的说明 17415789
捐赠科研通 5453969
什么是DOI,文献DOI怎么找? 2882339
邀请新用户注册赠送积分活动 1858992
关于科研通互助平台的介绍 1700658