气候变化领域集成服务门户(CCPortal): Predicting early mycotoxin contamination in stored wheat using machine learning

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DOI	10.1016/j.jspr.2024.102294
	Predicting early mycotoxin contamination in stored wheat using machine learning
	Kim, Yonggik; Kang, Seokho; Ajani, Oladayo Solomon; Mallipeddi, Rammohan; Ha, Yushin
发表日期	2024
ISSN	0022-474X
EISSN	1879-1212
起始页码	106
卷号	106
英文摘要	With continued global population growth and current rate of climate change, grain loss during storage remains a major contributor to postharvest losses of wheat ( Triticum aestivum L.). Infection with mycotoxin leads to degradation or even discarding of stored grain, causing economic losses and risks to food security. Deep learning models have been used in the agricultural domain for detecting prevalent diseases or contamination; however, data scarcity remains a critical bottleneck for rapid implementation of computer vision in this field. Herein, a compact convolutional transformer (CCT) - based model was applied to classify contaminated wheat by deoxynivalenol (DON) and aflatoxins (AFB 1 , AFB 2 , AFG 1 , and AFG 2 ), which was divided into three main classes: healthy, incipient, and contaminated. The classification was performed based on elevated CO 2 respiration rate (>= 31.20 +/- 0.62 mg CO 2 kg -1 h -1 ) and visual appearance of mold formation in initial and severe stage since the start the storage experiment. The proposed CCT model achieved an accuracy of 83.33%, with the contaminated class demonstrating the highest performance metrics, including precision (1.0), recall (0.90), and F1 -score (0.95), followed by the healthy and incipient classes. At the same time, explicit classification between the healthy and incipient classes deserves further improvement because it is highly relevant for the timely detection of spoilage and prevention of proliferation of mycotoxins in stored wheat.
英文关键词	Compact convolutional transformer; Mycotoxin; Predictive models; Respiration; Wheat storage
语种	英语
WOS研究方向	Entomology
WOS类目	Entomology
WOS记录号	WOS:001222140000001
来源期刊	JOURNAL OF STORED PRODUCTS RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297173
作者单位	Kyungpook National University; Kyungpook National University; Kyungpook National University
推荐引用方式 GB/T 7714	Kim, Yonggik,Kang, Seokho,Ajani, Oladayo Solomon,et al. Predicting early mycotoxin contamination in stored wheat using machine learning[J],2024,106.
APA	Kim, Yonggik,Kang, Seokho,Ajani, Oladayo Solomon,Mallipeddi, Rammohan,&Ha, Yushin.(2024).Predicting early mycotoxin contamination in stored wheat using machine learning.JOURNAL OF STORED PRODUCTS RESEARCH,106.
MLA	Kim, Yonggik,et al."Predicting early mycotoxin contamination in stored wheat using machine learning".JOURNAL OF STORED PRODUCTS RESEARCH 106(2024).