A combination of room cooling and the use of thermal insulation materials to maintain okra quality under simulated storage and transportation was evaluated. Okra pods were packed in plastic baskets and either cooled at 18 °C or not cooled in a room for 2 h. After either room cooling or no cooling, the okra pods were covered with three different materials: (1) perforated linear low-density polyethylene (P-LLDPE), (2) two layers of heat-reflective sheet with thin nonwoven (HRS+TNNW), and (3) metalized foam sheet (MFS). Typical handling (TP) without cooling and covering with P-LLDPE was used as the control. The six treatments were conducted during simulated storage (18 °C for 48 h) and transportation (30 °C for 15 h). Results showed that MFS gave the best insulation properties (Q_x and R-values), followed by HRS and TNNW. After room cooling, both HRS+TNNW and MFS materials delayed the time for pulp temperature to reach 18 °C (10 h), compared to P-LLDPE (2 h). TP presented the highest mass loss (17.8%) throughout simulated conditions, followed by cooling plus P-LLDPE (15.2%) and either of the thermal insulation materials with or without room cooling (3.6% to 5.2%), respectively. TP, cooling plus P-LLDPE, and no cooling plus MFS (44% to 56%) showed the highest percentage of decay, while cooling combined with both HRS+TNNW and MFS gave the lowest decay incidence (11–21%). Findings demonstrated that room cooling combined with HRS+TNNW had the highest efficiency for preserving cool temperature and reducing decay, compared to TP and room cooling plus MFS.

The efficiency of different thermal insulation covers in minimizing temperature fluctuations in cool chain management was investigated to reduce postharvest loss and maintain okra quality during storage and transportation. The four thermal insulation covering materials: (1) heat reflective sheet with thin nonwoven (HRS + TNNW), (2) heat reflective sheet with thick nonwoven (HRS + TKNW), (3) metalized Tyvek^® (MTyvek) and (4) metalized foam sheet (MFS) were studied and compared with perforated linear low-density polyethylene (P-LLDPE) as the typical handing package for okra distribution alongside no covering as the control. The material properties, transpiration rate, vital heat, temperature profiles (air and pulp temperatures), relative humidity, mass loss and incidence of decay were determined throughout a simulated supply chain. Results exhibited that HRS + TNNW and HRS + TKNW covers had the lowest thermal heat energy (Q_x) and moderate R-value. These two covers maintained low temperature fluctuation with the lowest rate of air and pulp temperature changes, reflecting in lowest mass loss and decay in okra. The HRS + TNNW cover yielded less decay (1%) in okra, compared to commercial covers; MTyvek (16%) and MFS (9%). Results showed that HRS + TNNW exhibited great potential as a thermal insulation cover to reduce postharvest loss in okra (5%) compared to typical handling (11–18%) and could be considered as alternative material to reduce the use of foam sheets in cool chain management distribution packaging of okra under ambient environment conditions.

Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power ($R^2\approx 0.88$) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions.