Conference Agenda

Traditional insect monitoring methods involve manual sampling and insect-trap inside silos which are incapable of continuous insect monitoring. A novel in-house fabricated microwave sensor proposed here is a self-sustaining resonator, with high QF >10⁵, based on inter-digital capacitor & coplanar waveguide. Working on principle, “cavity perturbation” it produces significant shift in resonance frequency with the tiny insect movement inside the measuring cavity. The sensor operating at 1.331 GHz (@ 20℃) frequency was tested against 5 common stored product insects. Also effect of different grain storage conditions – temperatures (20℃, 25℃, 30℃) and wheat moisture contents (13%, 14.5%, 16%) on sensor response was studied. The shift was positively influenced by the changes in environmental RH due to changes in dielectric properties. The shift in frequency was also directly correlated with insect type and activity. Like, red flour beetle (4mm) yielded 3.4± 0.5 GHz, while rusty grain beetle (2mm) gave 1.33 ± 0.32 GHz. For similar-sized insects, their activity measurement was used to differentiate. The sensor can detect even a single insect and has zero insect entrapment which allows very minimal maintenance. Overall, this novel, in-situ insect detection system is a potential addition to the smart grain bin monitoring system.

Big bags (raffia bags) fitted with plastic liners offer a practical alternative for residue-free pest control through modified atmospheres during grain storage and transport. This study evaluated the capability of a low-cost 70-µm monolayer polyethylene liner to generate lethal CO₂ conditions against Sitophilus oryzae. A commercial-scale experiment was conducted using nine big bags containing 700 kg of barley (12% m.c.). Sealed liners equipped with gas injection valves received CO₂ to reach initial concentrations of 85% and 95%, alongside an ambient control. Adult insects and infested grain samples were placed inside each bag to assess mortality of adults and immature stages. CO₂ concentration was monitored for 14 days to estimate concentration–time (CT) products. Gas concentrations declined to averages of 15% and 17% in the 85% and 95% treatments, respectively, reflecting permeability losses. Calculated CT products (8,493 and 9,900 %h) remained below the disinfestation threshold (12,600 %h), and immature survival was observed in two big bags. Nevertheless, complete adult mortality was achieved in all treated bags. Results indicate that inexpensive monolayer liners can effectively control adult populations, while full disinfestation may be attainable through higher initial CO₂ injection (e.g., 100%) or intermediate reinjection.

A comprehensive numerical framework was developed to investigate coupled transport phenomena in mixed-flow grain dryers by integrating computational fluid dynamics (CFD) with the discrete element method (DEM). The proposed CFD–DEM model explicitly resolves particle–fluid interactions, enabling simultaneous prediction of grain motion, airflow distribution, heat transfer, and moisture migration at the particle scale.

The formation of the grain bed and kernel movement were simulated using the proposed DEM model implemented in the commercial software PFC3D (Itasca Consulting Group, Minneapolis, MN). Heat and mass transfer and airflow patterns were solved using a CFD model developed in ANSYS Fluent 2022. Coupling between the DEM and CFD models was achieved through custom-coded algorithms in MATLAB (Version 8.4) using a quasi-static approach, in which simulations advanced incrementally in time and data were exchanged between the models after each time step.

Model validation was performed using experimental data reported in the literature, including grain temperature and moisture content distributions, particle flow behavior, and airflow patterns. Good agreement was achieved between simulated and experimental results, with average differences of 4.3% for grain temperature and 2.5% for grain moisture content.

Variability in moisture content among individual soybean grains may increase the risk of deterioration during storage when excessively wet grains are present within a lot, even when the commercial average moisture standard (12.5%) is met. Additionally, this heterogeneity negatively affects oil extraction efficiency in industrial processes such as extrusion–pressing. Conventional moisture determination methods either provide only average values or require slow and labor-intensive procedures, limiting real-time decision making.

The objective of this study was to develop a rapid method to estimate moisture variability at the individual grain level using near-infrared spectroscopy (NIR). Approximately 500 g samples from nine commercial soybean lots harvested in Balcarce, Argentina, were analyzed, with average moisture contents ranging from 11 to 16.6%. From each sample, 25 intact grains were randomly selected and individually scanned using an NIR instrument. Reference moisture content was determined by oven drying at 103 °C for 72 h. Calibration was developed using partial least squares (PLS) regression.

The model achieved a coefficient of determination (R²) of 0.96, indicating high predictive accuracy. The developed methodology enables rapid estimation of moisture variability among individual grains, providing a practical tool for rapid industrial suitability assessment and improved storage decision-making.

Sugar beet (Beta vulgaris L.) is commonly stored in large outdoor piles during the Winter, which can extend for up to five months. During storage, these beets are subjected to temperature fluctuations that can lead to multiple thawing and freezing cycles. These conditions, combined with metabolic heat generated through beet respiration, can create localized temperature increases within the pile, thereby accelerating spoilage and increasing storage losses. Effective aeration is therefore essential to maintain uniform temperature distribution within the pile and to prevent the formation of hotspots. The present study aimed to design and evaluate aeration duct configurations to improve airflow distribution in large sugar beet storage piles using Computational Fluid Dynamics (CFD). A 3D model was developed to simulate airflow patterns to predict the temperature profile of the sugar beet pile. To validate the model, temperatures obtained from the model were compared with the temperatures measured from onsite sugar beet piles during the storage seasons from October 2024 to February 2025 in the first season and from October 2025 to December 2025 in the second season in Taber, Alberta, Canada. The results showed that the CFD model predicted pile temperature with reasonable accuracy, demonstrating the benefits of optimizing aeration design.

Canola quality significantly deteriorates during storage, particularly under warm and humid conditions, leading to viability loss, oxidative and hydrolytic degradation. In this study the effectiveness of thermal pretreatments in improving canola storage stability was studied. Freshly harvested, dockage-free canola seeds were conditioned to three moisture contents (4, 8, and 14%) and stored under controlled conditions (5-35°C and relative humidity of 35% and 85%. Selected quality parameters, including germination, free fatty acid value (FAV), and peroxide value (PV), were analyzed biweekly over 12 weeks. Results showed that seed moisture dynamics were primarily governed by relative humidity. FAV and PV increased during storage, driven by high temperature, RH, and initial moisture. However, optimized mild thermal treatments significantly improved stability compared to the control. By week 12, PV decreased to 1.91±0.08 meq O₂/kg indicating effectiveness of the pretreatments. Furthermore, fine-tuned thermal processing retained higher viability under harsh storage conditions. Findings demonstrated that these pretreatments are practical stabilization techniques for maintaining canola seed quality.