Abstract:The use of silo and raffia bags for the temporary grain storage has been increasing in recent years. However, the methods for monitoring a stored product are limited to visual inspections and sampling. Thus, this research aimed to real-time equilibrium moisture content monitoring to predict grain quality of corn stored in different conditions in silo and raffia bags using wireless sensor network prototype, Internet of Things (IoT) platform, and neural network algorithms. Experiments were conducted using corn g… Show more
“…After calibration of the sensors at time zero, there was a reduction in temperature and an increase in relative humidity and intergranular CO 2 , with a tendency for the curves to stabilize over time. These findings demonstrate the consistency of the monitored variables, as well as the accuracy and functionality of the monitoring system 28 .…”
Section: Resultssupporting
confidence: 58%
“…After calibration of the sensors at time zero, there was a reduction in temperature and an increase in relative humidity and intergranular CO 2 , with a tendency for the curves to stabilize over time. These findings demonstrate the consistency of the monitored variables, as well as the accuracy and functionality of the monitoring system 28 . Figure 3 Temperature and relative humidity of corn grain mass with 12% ( A ), 16% ( B ), 25% ( C ) moisture contents in tube with holes of 6.5, 7.0 and 7.5 mm and drilling heights of 117.5, 235 and 470 mm.…”
Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis.
“…After calibration of the sensors at time zero, there was a reduction in temperature and an increase in relative humidity and intergranular CO 2 , with a tendency for the curves to stabilize over time. These findings demonstrate the consistency of the monitored variables, as well as the accuracy and functionality of the monitoring system 28 .…”
Section: Resultssupporting
confidence: 58%
“…After calibration of the sensors at time zero, there was a reduction in temperature and an increase in relative humidity and intergranular CO 2 , with a tendency for the curves to stabilize over time. These findings demonstrate the consistency of the monitored variables, as well as the accuracy and functionality of the monitoring system 28 . Figure 3 Temperature and relative humidity of corn grain mass with 12% ( A ), 16% ( B ), 25% ( C ) moisture contents in tube with holes of 6.5, 7.0 and 7.5 mm and drilling heights of 117.5, 235 and 470 mm.…”
Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis.
“…It was shown that temperature and water activity are important environmental factors affecting the growth of pathogenic fungi and mycotoxin production in paddy [ 9 ]. Lutz et al [ 10 ] used techniques such as wireless sensor network prototypes and neural network algorithms to predict the grain quality of corn stored in silos and raffia bags under different conditions, and experiments showed that storage time had an effect on grain quality decline for all factors. Therefore, it is crucial to maintain a proper storage environment to preserve the quality of paddy grains during storage.…”
The storage environment can significantly impact paddy quality, which is vital to human health. Changes in storage can cause growth of fungi that affects grain quality. This study analyzed grain storage monitoring data from over 20 regions and found that five factors are essential in predicting quality changes during storage. The study combined these factors with the FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting) model and k-medoids algorithm to construct a paddy quality change prediction model and a grading evaluation model, which showed the highest accuracy and lowest error in predicting quality changes during paddy storage. The results emphasize the need for monitoring and controlling the storage environment to preserve grain quality and ensure food safety.
“…Faree et al [2] used multiple linear regression and the artificial neural network (ANN) to predict the quality of maize grains during storage; they achieved better prediction results. Lutz et al [5] used a wireless sensor network, an IoT platform, to monitor the equilibrium moisture content in real time and used ANN to predict the quality of maize grains stored under different conditions. Szwedziak et al [6] used a proprietary computer application based on the RGB model to assess the contamination status of maize grains.…”
Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major staple grains, as the target grains whose storage monitoring data cover more than 20 regions, and constructed a grain storage process quality change prediction model, which includes a FEDformer-based grain storage process quality change prediction model and a K-means++-based grain storage process quality change grading evaluation model. We select six factors affecting grain quality as input to achieve effective prediction of grain quality. Then, evaluation indexes were defined in this study, and a grading evaluation model of grain storage process quality was constructed using clustering model with the index prediction results and current values. The experimental results showed that the grain storage process quality change prediction model had the highest prediction accuracy and the lowest prediction error compared with other models.
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