Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO₂ concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste.

Automatic 3D abdominal organ segmentation in magnetic resonance imaging presents a major challenge: data heterogeneity, caused by differences in acquisition protocol, contrast, spatial resolution, and imaging characteristics, which can degrade model generalization. This work investigates the use of FiLM (Feature-wise Linear Modulation) as a metadata-conditioning mechanism to improve the contextual adaptation of 3D segmentation networks. To this end, two reference architectures, U-Net and nnU-Net v2, were compared in both their baseline versions and their FiLM-conditioned variants. The evaluation was carried out on the CHAOS and AMOS datasets, as well as on a combined CHAOS+AMOS setting to analyze multi-domain robustness. The results show that the effect of FiLM clearly depends on the underlying architecture. In U-Net, FiLM produced favourable quantitative trends in the final post-processed predictions, with a small gain in CHAOS, a clearer improvement in AMOS, and the largest effect in the combined scenario, where the increase reached +0.0215 in clean Dice, together with reductions of −6.66 mm in HD95 and −3.85 mm in ASD. However, these improvements were not uniform across all metrics and organs. In contrast, in nnU-Net the conditioning did not provide consistent benefits and reduced performance in some scenarios, particularly in AMOS. In addition, when post-processing was applied to remove islands and disconnected components, its impact was much greater in U-Net than in nnU-Net, indicating that the latter produced cleaner and more stable segmentations directly from the raw output. Overall, this work suggests that FiLM can be useful to reinforce more generic architectures such as U-Net, especially when problem heterogeneity increases, but it was not consistently beneficial for nnU-Net, whose baseline already constituted the most robust and best-balanced model in the study. These results support the idea that the value of metadata conditioning is not universal, but instead depends on the base architecture and the generalization scenario under consideration.

Durante la fase de poscosecha y antes del consumo, los productos perecederos pueden perder calidad por malas condiciones de conservación. La monitorización de las condiciones ambientales clave puede predecir estas pérdidas. Este proyecto presenta un datalogger autónomo, portátil y compacto para monitorizar parámetros ambientales en tiempo real durante la cadena de suministro. El dispositivo mide temperatura, humedad relativa, CO2, luminosidad y vibraciones, y transmite los datos de forma inalámbrica, simplificando soluciones existentes que emplean varios dispositivos y presentan costes prohibitivos. Se han empleado estrategias para optimizar el consumo de energía, logrando un mes de autonomía para cubrir periodos enteros de transporte de alimentos. El datalogger utiliza NB-IoT y otras tecnologías inalámbricas para enviar los datos a una plataforma en la nube. Se han realizado ensayos de autonomía, comparación con otros dataloggers comerciales y ensayos de campo satisfactorios para validar su funcionamiento y transmisión de datos en tiempo real durante un transporte refrigerado.