Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production.

Milk quality has a growing importance for farmers as component-based pricing becomes more widespread. Food quality and precision manufacturing techniques demand consistent milk composition. Udder health, general cow condition, environmental factors, and especially feed composition all influence milk quality. The large volume of routinely collected milk data can be used to build prediction models that estimate valuable constituents from other measured parameters. In this study, casein was chosen as the target variable because of its high economic value. We developed a multiple linear-regression model and a feed-forward neural network model to estimate casein content from twelve commonly recorded milk traits. Evaluated on an independent test set, the regression model achieved R² = 0.86 and RMSE = 0.018%, with mean bias = +0.003% and slope bias = −0.10, whereas the neural network improved performance to R² = 0.924 and RMSE = 0.084%. In silico microgreen inclusion from 0% to 100% of dietary dry matter raised the predicted casein concentration from 2.662% to 3.398%, a relative increase of 27.6%. To extend practical applicability, a simulation module was created to explore how microgreen supplementation might modify milk casein levels, enabling virtual testing of dietary strategies before in vivo trials. Together, the predictive models and the microgreen simulation form a cost-effective, non-invasive decision-support tool that can accelerate diet optimization and improve casein management in precision dairy production.

The Daily Light Integral (DLI) maps of Portugal are decision-support tools to provide objective assessments of light availability for plants. These maps allow for the analysis of the spatial and seasonal distribution patterns of photosynthetically active radiation (PAR, 400–700 nm). A semi-automatic DLI mapping workflow was elaborated and DLI maps with two different scales (2 and 5 mol·m⁻²·d⁻¹) were created for Portugal’s mainland, analyzing from regional (Northern regions, Central regions, Southern regions) and seasonal (Spring, Summer, Autumn, Winter) perspectives. The DLI values and ranges in Portugal provide a clear spatial gradient from north to south, influenced by geographic location, topography, and climate. In autumn, the DLI values decrease (11–41 mol·m⁻²·d⁻¹) and the lowest DLI values are observed in the winter months (7–17 mol·m⁻²·d⁻¹). During spring, DLI values increase, reaching their peak in the summer months (41–57 mol·m⁻²·d⁻¹). In autumn the DLI range is narrow (11–13 mol·m⁻²·d⁻¹), as well as in winter (8–12 mol·m⁻²·d⁻¹), and it becomes broader during spring and summer (11–14 mol·m⁻²·d⁻¹). To enhance agricultural practices, future DLI maps should be integrated with agro-climatic maps and thematic layers such as soil, water, topography, temperature, and biodiversity. These integrated maps should then be incorporated into agricultural policy and decision-making to improve crop management strategies. Portugal’s agricultural lighting strategy will utilize DLI maps alongside crop-specific recommendations, shading management, local conditions and market demands.