Food security is an influential factor for the development and future of humanity. Stored grain temperature is a key physical variable for grain quality control and management in stored-grain ecosystem. In this study, a prediction model using Fourier series based on least-squares method is introduced to describe the average temperature movement of grain pile and the daily air temperature, naming hysteresis cycle model (HCM). Then the HCM based on the Fourier analysis is proposed that incorporating with the least-squares method, the Fourier series model is applied to forecast the grain pile temperature from the daily air temperature. The HCM is built by finding relationship called conversion coefficients between the air and grain pile temperature that is constructed on the basis of Fourier analysis, and the optimal order of the model is determined by Akaike information criteria. Our method can reflect the time lag of the grain pile temperature changes with the change of air temperature and requires only a single variable (i.e. air temperature). Experiments including model fitting, temperature prediction, and results comparison for a period of 425 days from a real-world granary, yield satisfied results agreeing well with actual observation values. As a result of model validation, the proposed method has the root mean square error (RMSE) values ranging from 1.6°C to 2.3°C with average RMSE of 1.9°C. In other words, the proposed model can predict grain pile temperature with a high accuracy once an acceptable relationship between air temperature and grain pile temperature has been constructed from previously temperature data. Result analysis indicates effectiveness and workability of the proposed model for stored grain temperature prediction.

Starch, alginate, and poly(N-isopropylacrylamide) (PNIPAAm) were combined to prepare a semi-interpenetrating network (IPN) hydrogel with temperature sensitivity. Calcium chloride was used as cross-linking agent, the non-toxigenic Aspergillus flavus spores were successfully encapsulated as biocontrol agents by the method of ionic gelation. Characterization of the hydrogel was performed by Fourier-transform infrared spectroscopy (FTIR), scanning electron micrograph (SEM), and thermogravimetry analysis (TGA). Formulation characteristics, such as entrapment efficiency, beads size, swelling behavior, and rheological properties were evaluated. The optical and rheological measurements indicated that the lower critical solution temperature (LCST) of the samples was about 29–30 °C. TGA results demonstrated that the addition of kaolin could improve the thermal stability of the semi-IPN hydrogel. Morphological analysis showed a porous honeycomb structure on the surface of the beads. According to the release properties of the beads, the semi-IPN hydrogel beads containing kaolin not only have the effect of slow release before peanut flowering, but they also can rapidly release biocontrol agents after flowering begins. The early flowering stage of the peanut is the critical moment to apply biocontrol agents. Temperature-sensitive hydrogel beads containing kaolin could be considered as carriers of biocontrol agents for the control of aflatoxin in peanuts.

For the wise use of fungal biocontrol and metalaxyl fungicide, starch–alginate-based formulations have been developed by encapsulating metalaxyl and non-toxigenic Aspergillus flavus spores simultaneously in the form of microspheres using calcium chloride as a cross-linking agent. The formulations were characterized by Fourier transform infrared spectroscopy (FTIR), a scanning electron micrograph (SEM), and thermogravimetry (TGA). Formulation characteristics, including the bead size, entrapment efficiency, swelling ratio of the beads, and rheological properties, were analyzed. The release behavior of beads with different formulations was evaluated. The addition of kaolin and rice husk powder in starch–alginate beads retarded the release profile of spores and metalaxyl. The release of the active ingredient from starch–alginate–kaolin beads and starch–alginate–rice husk powder beads occurred in both a controlled and sustained manner. Additionally, the release rate decreased with the increase of kaolin or rice husk powder content. The beads added with kaolin were slower than the release of rice husk powder. In comparison, spores released slower and lasted longer than metalaxyl. The starch–alginate–kaolin formulations could be used as controlled release material in the field of biocontrol and reduce the harm of fungicides to the environment.

The consumption of agricultural products contaminated with mycotoxins poses a significant threat to the health of both humans and animals. Maize frequently becomes contaminated with toxic fungi while it is still growing in the field. Therefore, more proactive measures should be implemented to reduce mycotoxin levels during the storage and processing of maize after harvest. This article analyzes the prevalent mycotoxins found in maize, specifically aflatoxins, ochratoxins, trichothecenes, fumonisins, and zearalenone. The study provides a comprehensive analysis of the occurrence of mycotoxins in maize during storage, as well as fate analysis of them during processing. It summarizes the impacts of storage time, environmental conditions, storage methods, and agricultural practices on mycotoxin occurrence during storage in the post-harvest period. Furthermore, the different distribution of mycotoxins across various fractions during both dry- and wet-milling processes in the post-harvest processing period is analyzed. Additionally, the strategies to control mycotoxins in maize are also proposed during the post-harvest period. This review offers valuable insights for future research on mycotoxin contamination in maize during the post-harvest period.

In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert. This approach combines two advanced three-dimensional deep learning models, a grain storage state classification model based on 3D DenseNet and a temperature field prediction model based on 3DCNN-LSTM. First, the grain storage state classification model based on 3D DenseNet efficiently extracts features from three-dimensional grain temperature data to achieve the accurate classification of storage states. Second, the temperature prediction model based on 3DCNN-LSTM incorporates historical grain temperature and absolute water potential data to precisely predict the dynamic changes in the granary’s temperature field. Finally, the grain temperature prediction results are input into the 3D DenseNet to provide early warnings for potential condensation and mildew risks within the grain pile. Comparative experiments with multiple baseline models show that the 3D DenseNet model achieves an accuracy of 97.38% in the grain storage state classification task, significantly outperforming other models. The 3DCNN-LSTM model shows high prediction accuracy in temperature forecasting, with MAE of 0.24 °C and RMSE of 0.28 °C. Furthermore, in potential risk alert experiments, the model effectively captures the temperature trend in the grain storage environment and provides early warnings, particularly for mildew and condensation risks, demonstrating the potential of this method for grain storage safety monitoring and risk alerting. This study provides a smart grain storage solution which contributes to ensuring food safety and enhancing the efficiency of grain storage management.