利用传感器和机器学习模型,在实际规模上监测和预测采用不同干燥和储存技术的大豆的质量

IF 2.7 2区 农林科学 Q1 ENTOMOLOGY
Roney Eloy Lima , Paulo Carteri Coradi , Dágila Melo Rodrigues , Paulo Eduardo Teodoro , Larissa Pereira Ribeiro Teodoro , Dalmo Paim de Oliveira
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引用次数: 0

摘要

在谷物干燥和储藏操作中应用与人工智能相关的监测技术有助于决策过程,防止谷物变质。因此,本研究的目的是评估不同干燥技术(连续干燥和干燥筒仓)和储存方法(垂直和水平筒仓)对大豆质量的影响,并利用机器学习算法预测谷物质量的变化。在此过程中监测到的环境和粒间变量与谷物的物理和化学质量参数相关联,以便通过机器学习模型进行预测。平衡含水量超过 12% 会增加储藏谷物的呼吸作用,使二氧化碳浓度水平超过 600 ppm,导致大豆质量发生变化,干物质损失从 4% 增加到 14%,表观质量比降低到 650 kg m-3,发芽率降低到 60%,出油率降低到 18%,粗蛋白降低到 34%,电导率增加到 500 mS cm-1 g-1,酸度油增加到 7 mg KOH g-1。据观察,在烘干机-贮藏条件下烘干的谷物在加工结束时保持了最高的谷物品质。虽然谷物品质的变化与所采用的干燥和储藏技术有关,但人工神经网络模型在预测谷物品质方面表现出更优越的性能。因此,建议对大豆进行收获后干燥,随后将谷物储存在干燥器-硅仓中,同时监测环境和粒间变量。建议将这种方法与人工神经网络模型结合使用,以更高效地预测损失和提高谷物品质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Monitoring and predicting the quality of soybeans for different drying and storage technologies on a real scale using sensors and Machine Learning models

Monitoring and predicting the quality of soybeans for different drying and storage technologies on a real scale using sensors and Machine Learning models

The application of monitoring techniques associated with artificial intelligence in grain drying and storage operations can assist in decision-making processes, preventing deterioration. Therefore, the objective of this study was to evaluate the effects of different drying technologies (continuous drying and dryer-silos) and storage methods (vertical and horizontal silos) on the quality of soybeans associated with Machine Learning algorithms to predict changes in grain quality. The environmental and intergranular variables monitored during the processes were correlated with physical and chemical quality parameters of the grains for prediction through Machine Learning models. The equilibrium moisture content above 12% increased the respiration of the stored grain mass to CO2 concentration levels above 600 ppm, causing changes in the soybean quality from 4 to 14% dry matter loss, and reducing the apparent mass specific at 650 kg m−3, germination at 60%, oil yield reduced at 18%, and crude protein at 34%, as well as increased the electrical conductivity at 500 mS cm−1 g−1, the acidity oil increased at 7 mg KOH g−1. It was observed that grain subjected to drying and storage conditions in dryer-silos maintained the highest grain quality at the end of the process. Although there were differences related on the applied drying and storage technology regarding changes in grain quality, it was noticed that the Artificial Neural Networks model demonstrated superior performance in predicting grain quality. Thus, it is recommended to conduct post-harvest drying of soybeans and subsequent grain storage in dryer-silos, while monitoring environmental and intergranular variables. This approach is advised to be coupled with the utilization of Artificial Neural Networks models to anticipate losses and enhance grain conversation with greater efficiency.

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来源期刊
CiteScore
5.70
自引率
18.50%
发文量
112
审稿时长
45 days
期刊介绍: The Journal of Stored Products Research provides an international medium for the publication of both reviews and original results from laboratory and field studies on the preservation and safety of stored products, notably food stocks, covering storage-related problems from the producer through the supply chain to the consumer. Stored products are characterised by having relatively low moisture content and include raw and semi-processed foods, animal feedstuffs, and a range of other durable items, including materials such as clothing or museum artefacts.
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