Positron Emission Tomography (PET) is increasingly employed for precision monitoring in plant science, as it is a non-invasive technique for the quantitative analysis of the functional mechanisms of plant metabolism. When exported to digital agronomy in combination with qualitative detection, PET has the potential to drive the sustainable management of chemical fertilizers. Such a translational approach requires a transition from controlled in-lab to natural in-field PET imaging. We report a novel portable and shape-adaptable digital plant PET system based on the Multi Voltage Threshold digital readout, with an extendable transverse Field of View (FOV) ranging between 80 mm and 120 mm. The system reaches a peak sensitivity up to (16.94 ± 0.01)% and (9.12 ± 0.01)% in closed and maximally opened FOV configuration, respectively. Image quality has been tested with a dedicated plant phantom imaging. We verified the ability of the prototype to perform plant PET imaging: in-lab with Zea mays L . sprouts under heat stress, and in-field with Triticum aestivum sprouts under drougt stress.

The starch and moisture content of saffron corms are critical indicators of their flowering potential and yield. This study investigated the use of rapid, minimally invasive VNIR reflectance spectroscopy measurement to assess these parameters. The measurements were used to develop predictive models through four machine learning algorithms (PLSR, RF, SVR, and GPR). Spectral data were obtained from 130 fresh corm samples. Wavelength analysis identified key starch-sensitive intervals (~930–1000 nm and ~1150–1220 nm) and a broad moisture-sensitive region (~900–1350 nm). Among the evaluated models, the combination of the multiplicative scatter correction pre-processing method and Gaussian process regression (MSC-GPR) demonstrated the optimal predictive performance for water content (R² = 0.92, RMSE = 0.71%, RPD = 4.56, RPIQ = 5.37), and the combination of the MSC method and partial least squares regression (PLSR-MSC) demonstrated moderate performance for starch content (R² = 0.73, RMSE = 28.7 mg g⁻¹, RPD = 2.14, RPIQ = 2.81, dry weight). These results demonstrate the viability of VNIR spectroscopy as a minimally invasive tool for the pre-planting assessment of saffron corm quality under laboratory conditions. The method provides a laboratory-based framework for corm screening and selection, with potential for future adaptation to field settings using portable spectrometers following expanded calibrations and advanced modeling techniques.

The starch and moisture content of saffron corms are critical indicators of their flowering potential and yield. This study investigated the use of rapid, non-destructive vis-NIR reflectance spectroscopy measurement to assess these parameters. The measurements were used to develop predictive models through four machine learning algorithms (PLSR, RF, SVR, and GPR). Spectral data were obtained from 130 fresh corm samples. Wavelength analysis identified key starch-sensitive intervals (~930–1000 nm and ~1150–1220 nm) and a broad moisture-sensitive region (~900–1350 nm). Among the evaluated models, the partial least squares regression (PLSR) model demonstrated the optimal predictive performance for moisture (R² = 0.89, RMSE = 0.91%, RPD = 3.67, RPIQ = 4.91) and moderate performance for starch (R² = 0.68, RMSE = 26.29 mg g⁻¹, RPD = 1.87, RPIQ = 2.37, dry weight). These results demonstrated the viability of VIS-NIR spectroscopy as a viable, non-destructive tool for the pre-planting assessment of saffron corm quality. The method provides a practical foundation for corm screening and selection, with potential for further improvement in starch prediction through expanded calibrations and advanced modeling techniques.

While vertical farming may offer high productivity per unit land area and has received increasing attention for urban food production, research has primarily focused on leafy greens and herbs. Although recent studies have also introduced fruiting vegetables and cereal crops, comparative evaluations under standardized environmental conditions remain limited because crop morphology, duration, harvestable organs, and biomass allocation differ substantially among crop types. This study, conducted in AlmaVFarm, the experimental vertical farm of the University of Bologna, evaluated the productivity and resource use efficiency of five representative crops grown under identical environmental conditions. Crops included dwarf corn as a staple crop, dwarf tomato as a fruit crop, basil as an herb, and lettuce and arugula as leafy greens. To enable meaningful cross-crop evaluations, performance was assessed using normalized indicators of land-surface, cultivation volume, light, energy, and cost use efficiencies. Lettuce achieved the highest annual marketable yield (73.2 kg FW m⁻² y⁻¹), producing 46% more than arugula and 35% more than basil. It also exhibited the greatest land-surface use efficiency (65 kg m⁻² y⁻¹), volume use efficiency (24.5 kg m⁻³ y⁻¹), and energy use efficiency (46.7 g FW kWh⁻¹), corresponding to 3.5-fold and 58-fold higher energy efficiency than tomato and corn, respectively, and up to 1.8-fold higher than arugula and basil. The normalized resource use efficiency framework enabled robust comparisons among contrasting crop categories despite their distinct biological characteristics, revealing substantial differences in adaptation to indoor cultivation. These findings provide a quantitative basis for crop selection and diversification strategies in vertical farming while highlighting the need for crop-specific optimization to improve the resource use efficiency and commercial viability of fruit and staple crops in CEA.