Ensuring safety of grain in storage

18 January 20217 min reading

Dr. Stathis Kaloudis Senior Engineer, Centaur Analytics, Inc. Centaur Analytics, Inc.

Grain in storage is susceptible to threats like pest infestation and mold development that can lower the quality of the grain, it’s marketability, and safety. To ensure the safety of grain in storage one must thoroughly evaluate factors such as grain temperature, moisture content, carbon dioxide concentrations, weather conditions as well as their interaction and impact on grain in a storage structure. Novel monitoring and forecasting tools are available to assist storage managers to detect and resolve efficiently challenging issues.


Most grain damage that occurs during storage is caused by molds and insects. It’s critical to identify the factors that influence their growth to develop the appropriate equipment and computational tool to accurately monitor and predict their evolution on stored grain. The following two sections describe briefly the conditions that favor insect infestation and mold development: a) insect infestation of grain Temperature and moisture conditions in grain stores affect the population growth rate of insect pests by impacting on development rate, fecundity, and survivorship. Like the population growth rate, the development rate increases from a lower threshold up to the optimum temperature and then declines rapidly (Figure 1). At low temperatures, the population growth rate is generally more sensitive to changes in development rate than it is to influences of fecundity and survivorship. It is also relatively insensitive to grain moisture content. On the other hand, at optimum temperatures (about 33 oC), grain moisture content has a much greater effect on the population growth rate. This is largely due to its effect upon fecundity rather than on development rate [Driscoll, 2000].

Figure 1. The relationship between temperature and intrinsic rate of increase for Rhyzopertha dominica at 70% relative humidity [Driscoll, 2000]. Grain temperatures near 33 oC are optimum for rapid reproduction.

b) Mold development and mycotoxin production The significant factors that determine whether grain in storage would be invaded sufficiently by fungi are: • grain moisture content • grain temperature • storage period • number of broken grains and foreign materials present • the degree to which the grain already has been invaded by fungi before it arrives at a given site • presence of insects and mites Among the fungal species that may contaminate the grain, some of them will be able to produce mycotoxins (Figure 2).

It becomes evident that to address the above challenges a grain status monitoring system should be able to: • sense and predict accurately temperature, relative humidity, CO2/O2/fumigant gases • provide early spoilage detection, grain quality forecast • propose the best aeration fumigation strategies


Traditional methods of grain monitoring systems employ sensors that are hardwired into the storage structure or by probing the grain with an electronic sensor device. These methods are inefficient since most of the time not all the parameters of interest are being recorded and the cost for supplementary sensors and installation is substantially high. Furthermore, the use of models predicting grain condition is quite limited, on top of the fact that such models are simplified and have limited accuracy. Subsequently, early detections of spoilage are often not possible thus aeration planning is based on insufficient information. For fumigation applications, toxic gas sensors that use suction are bulky, hard to use, have low sample frequency and often involve human interaction making them prone to human error.


Recent advancements in grain storage technology [Centaur, 2020] enabled the development of wireless sensors (Figure 3), that measure all the parameters that affect grain quality (e.g., temperature, relative humidity, O2, and CO2 gasses).

Figure 2. Classification of the stored grain fungal species in four eco-physiological groups: i) hygrophilic group I (e.g., Fusarium spp., Epicoccum sp., Alternaria sp., Rhizopus sp., Mucor sp.); ii) cryotolerant group II (e.g., Penicillium verrucosum, Paecilomyces sp., other Penicillia); iii) thermotolerant group III (e.g., Aspergillus flavus, A. candidus, A. nidulans); iv) xerotolerant group IV (e.g., Eurotium spp., Aspergillus spp.) [Fleurat-Lessard, 2017].

Additionally, based on a scientific study by USDA researchers [Brabec, 2019], they are suitable for fumigation applications (e.g., phosphine) since they measure fumigant concentrations and help fumigation managers to better evaluate fumigations and assure successful insect control. They also transmit safely their data to the cloud in real-time, offering worldwide accessibility. Their advantages also include the ability to move with the grain, in case of grain transit, offering enhanced traceability.


Complementary with the hardware, further improvements have been accomplished in mathematical modeling of methods for silo microclimate and fumigation applications. Cognitive data analytics and advanced mathematical models can predict the condition of the grain or the concentration of the fumigant in every location inside the storage volume.

b) Pest Management Utilizing the capabilities of advanced numerical modeling in fumigation applications and constant feedback from wireless sensors the phosphine concentration could be determined for every location inside the grain bulk and at any given time based on all the factors that occasionally affect the toxicity of the fumigant and prevent treatments to be successful. Centaur Analytics, Inc. has developed [Agrafioti, 2020] a unique system considering all the relevant parameters:

Figure 3: Example of wireless sensor installation inside a grain silo

• The degassing rate of metal phosphides (Mg3P2, AlP) depends on temperature and relative humidity values. • Weather conditions to evaluate accurately the storage interaction with its surroundings in terms of heat transfer, gas losses, and movement • Absorption of Phosphine gas by grain at differing rates depending on the commodity which can reduce the concentrations of fumigation doses to sublethal levels before grain has been disinfected. • Insect species and resistance • Post-fumigation-aeration and personnel safety can be measured in real-time. As the cognitive software correlates phosphine exposure with insect mortality, a methodology for planning precision fumigations can be established (Figure 4). Users receive notifications for the fumigation duration and when success could be enabled.

b) Predicting Mold Development In view of the above sections, in-depth knowledge and understanding of silo microclimate is crucial. An efficient method for tackling this is through the combination of field measurements and computer simulation based on Computational Fluid Dynamics (CFD) models. A CFD model is developed to solve and analyze problems that involve fluid flows, heat transfer, moisture migration and grain respiration. Furthermore, models evaluating grain deterioration are integrated into the software. These models offer predictions of safe storage time based on losses in grain dry matter, the possibility of mold appearance and the reduction of germination capacity (Figure 4).

Figure 4: User interface of a web platform predicting the outcome of a phosphine fumigation.


The monitoring capabilities of wireless sensors with the complementary use of advance forecasting models, offer an in-depth knowledge of the grain temperature, moisture content, risk of mold development and insect infestation throughout the storage period, bringing unprecedented benefits to the users: • prevention of mold development and mycotoxin contamination • cost-effective prevention strategies for mold development • cost reduction of the overall pest control application, avoiding excessive chemicals (e.g., overdosing, need to repeat failed fumigations) as well as excess labor • higher commodity market value • supply chain transparency

Figure 4: A user interface of a web platform that presents the grain conditions at every location in a silo, including forecast values (left). On the right the safe storage time (in days) is displayed based on several quality metrics, such as the dry matter loss, the appearance of visible molds, and the germination capacity.


Agrafioti, P.; Kaloudis, E.; Bantas, S.; Sotiroudas, V.; Athanassiou, G., Modeling the distribution of phosphine and insect mortality in cylindrical grain silos with Computational Fluid Dynamics: Validation with field trials. Computers and Electronics in Agriculture, Volume 173, 2020 ( Brabec D, Campbell J, Arthur F, Casada M, Tilley D, Bantas S. Evaluation of Wireless Phosphine Sensors for Monitoring Fumigation Gas in Wheat Stored in Farm Bins. Insects. Volume 10, Issue 5, 2019 ( Centaur Analytics Inc., 2020, Driscoll R, B.C Longstaff, S Beckett, Prediction of insect populations in grain storage, Journal of Stored Products Research, Volume 36, Issue 2, 2000, Fleurat-Lessard F., Integrated management of the risks of stored grain spoilage by seedborne fungi and contamination by storage mould mycotoxins - An update, Journal of Stored Products Research 71 (2017),

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