Adaptation of technological packaging for conservation of soybean seeds in storage units as an alternative to modified atmospheres

J Food Process Engineering

Jaques

et al. 2022

Self Cite

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 grain with two initial water contents of 13% and 18% (w.b.), three storage environments with temperatures of 30, 23, and 17 C, and two types of packaging, that is, silo and raffia bags, for a 3-month storage evaluation. During the monitoring

Section: Resultssupporting

confidence: 91%

supporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in silo and raffia bags

Lutz

J Food Process Engineering

Jaques

et al. 2022

Self Cite

“…The results observed in the correlation analysis are consistent with the behavior of the qualitative variables in relation to the treatment factors established for corn grain storage. The results corroborate those obtained by Coradi et al 37 .…”

Section: Resultssupporting

confidence: 93%

Sensor-cable-probe and sampler for early detection and prediction of dry matter loss and real-time corn grain quality in transport and storage

Nunes

Jaques

et al. 2023

Sci Rep

Self Cite

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.

“…High percentages of seed germination are obtained over storage time when seeds are stored in proper temperatures and packaging 39 . Coradi et al 40 verified that the artificially cooled soybean seeds maintained their physiological quality for 140 days of storage. Coradi et al 41 observed that seeds stored in uncontrolled environments obtained increased respiration rate and accelerated deterioration.…”

Section: Resultsmentioning

confidence: 96%

Predicting the quality of soybean seeds stored in different environments and packaging using machine learning

André

Teodoro

et al. 2022

Sci Rep

Self Cite

The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optimize the processes and obtain accurate results for decision-making in the seed storage process. This study aimed to analyze the performance of ML algorithms from variables monitored during seed conditioning (temperature and packaging) and storage time to predict the physical and physiological quality of stored soybean seeds. Data analysis was performed using the Artificial Neural Networks, decision tree algorithms REPTree and M5P, Random Forest, and Linear Regression. In predicting seed quality, the combination of the input variables temperature and storage time for REPTree and Random Forest algorithms outperformed the linear regression, providing higher accuracy indices. Among the most important results, it was observed for apparent specific mass that T + P + ST, T + ST, P + ST, and ST had the highest r means and the lowest MAE means, however, Person's r coefficient for these inputs was 0.63 and the MAE between 9.59 to 10.47. The germination results for inputs T + P + ST and T + ST had the best results (r = 0.65 and r = 0.67, respectively) in the ANN, REPTree, M5P and RF models. Using computational intelligence algorithms is an excellent alternative to predict the quality of soybean seeds from the information of easy-to-measure variables.