Computer vision by unsupervised machine learning in seed drying process

IF 1.2 4区 农林科学 Q2 AGRICULTURE, MULTIDISCIPLINARY Ciencia E Agrotecnologia Pub Date : 2023-07-03 DOI:10.1590/1413-7054202347018922
R. D. Pinheiro, G. I. Gadotti, Ruan Bernardy, Rafael Rico Tim, Karine Von Ahn Pinto, Graciela Buck
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引用次数: 1

Abstract

ABSTRACT Analyzing the impact of harvest-time drying data is crucial for successful storage and maintaining regulatory seed quality. This study aimed to assess the performance of fixed and mobile dryers using machine learning techniques. Data were collected from convective dryers, including the total number of dryers used, drying time (in hours), moisture percentages at the product’s entrance and exit, and the humidity difference between them. The study employed the Filtered Clusterer model, which utilizes the Simple K-Means technique and the Resample filter to group data based on similarities. The findings indicated distinct differences between fixed and mobile drying systems, with well-defined variations within each system. The algorithm, combined with the applied filters, proved effective in unsupervised classification by identifying and reducing inter-cluster similarity within the fixed system, thereby creating distinct classes within the dataset. In conclusion, the algorithm successfully clustered the scattered dataset and accurately classified and minimized inter-cluster similarity within the fixed system. Conversely, the mobile system exhibited low drying efficiency.
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无监督机器学习在种子干燥过程中的应用
摘要分析收获期干燥数据对种子成功贮藏和保持籽粒质量的影响至关重要。本研究旨在利用机器学习技术评估固定和移动烘干机的性能。从对流干燥机上收集数据,包括使用的干燥机总数,干燥时间(小时),产品入口和出口的水分百分比,以及两者之间的湿度差。该研究采用了Filtered Clusterer模型,该模型利用Simple K-Means技术和ressample过滤器根据相似性对数据进行分组。研究结果表明,固定和移动干燥系统之间存在明显差异,每个系统内部都有明确的变化。该算法与应用的过滤器相结合,通过识别和降低固定系统内的聚类间相似性,从而在数据集中创建不同的类,证明了该算法在无监督分类中是有效的。综上所述,该算法成功地对分散的数据集进行了聚类,并对固定系统内的聚类相似性进行了准确的分类和最小化。相反,移动系统的干燥效率较低。
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来源期刊
Ciencia E Agrotecnologia
Ciencia E Agrotecnologia 农林科学-农业综合
CiteScore
2.30
自引率
9.10%
发文量
19
审稿时长
6-12 weeks
期刊介绍: A Ciência e Agrotecnologia, editada a cada 2 meses pela Editora da Universidade Federal de Lavras (UFLA), publica artigos científicos de interesse agropecuário elaborados por membros da comunidade científica nacional e internacional. A revista é distribuída em âmbito nacional e internacional para bibliotecas de Faculdades, Universidades e Instituições de Pesquisa.
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