Lettuce is widely used for its healthy properties, and it is of interest to increase them with minimal environmental impact. The purpose of this work was to evaluate the effect of the arbuscular mycorrhizal fungus (AMF) Funneliformis mosseae in lettuce plants (Lactuca sativa L. cv. Salinas) cultivated in a soilless system with sub-optimal phosphorus (P) compared with non-inoculated controls at two different P concentrations. Results show that lettuce inoculation with the selected AMF can improve the growth and the nutritional quality of lettuce even at sub-optimal P. Leaf content of chlorophylls, carotenoids, and phenols, known as important bioactive compounds for human health, was higher in mycorrhizal lettuce plants compared with non-mycorrhizal plants. The antioxidant capacity in AMF plants showed higher values compared with control plants grown at optimal P nutrition level. Moreover, leaf gas exchanges were higher in inoculated plants than in non-inoculated ones. Nitrogen, P, and magnesium leaf content was significantly higher in mycorrhizal plants compared with non-mycorrhizal plants grown with the same P level. These findings suggest that F. mosseae can stimulate plants growth, improving the nutritional quality of lettuce leaves even when grown with sub-optimal P concentration.

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having (non-linear) sigmoid-type dynamics, are instances of the two classes observed and missing, respectively. Considering that the time series of the logistic sigmoid function is the solution to a reciprocal linear dynamic model, the exact expectation-maximization algorithm can be applied to infer the hidden states and to learn the parameters of the model. At iterative convergence, the parameter estimates are then used to derive a predictor of the measurement data several days ahead. To evaluate the performance of the proposed DBN, we followed three cultivation cycles of micro-tomatoes (MicroTom) in a mini-greenhouse. The environmental parameters were temperature, converted into Growing Degree Days (GDD), and the solar irradiance, both at a daily granularity. The measurement data were Leaf Area Index (LAI) and Evapotranspiration (ET). Although measurement data were only available scarcely, it turned out that high quality measurement data predictions were possible up to three weeks ahead.

The aim of this research was to develop an effective protocol for the application of nonthermal plasma (NTP) technology to the hydroponic nutrient solution, and to investigate its effects on the growth and quality of baby leaf lettuce (Lactuca sativa var. acephala Alef.) grown in a hydroponic growing system (HGS) specifically designed for indoor home cultivation. Four HGSs were placed in separate growth chambers with temperature of 24 ± 1 °C and relative humidity of 70 ± 5%). Lettuce plants were grown for nine days in nutrient solutions treated with NTP for 0 (control) to 120 s every hour. Results of the first experiments showed that the optimal operating time of NTP was 120 s h⁻¹. Fresh leaf biomass was increased by the 60 and 120 s NTP treatments compared to the control. Treating the nutrient solution with NTP also resulted in greater leaf content of total chlorophylls, carotenoids, total phenols, and total antioxidant capacity. NTP also positively influenced chlorophyll a fluorescence in Photosystem I (PSI) and photosynthetic electron transport. These results revealed that the NTP treatment of the nutrient solution could improve the production and quality of hydroponically grown baby leaf lettuce.

Tomatoes are globally renowned for their nutritional value and culinary versatility. However, environmental stresses, particularly salinity, present significant challenges to tomato production, impacting both yield and fruit quality. In light of these challenges, this study investigates the effect of salinity on yield and fruit quality of a local cultivar tomato named ‘Pisanello’ in a closed soilless rockwool cultivation system. Total yield, fruit size, and number were investigated in both control (10 mM of NaCl) and salinity-treated plants (salinity 1 (S1)~30 mM of NaCl and salinity 2 (S2)~60 mM of NaCl), alongside various physicochemical parameters in fully ripened tomato fruits. The results indicated a decrease in crop production with rising sodium chloride concentration in the nutrient solution (25% and 41% for S1 and S2 treatment, respectively). Conversely, salinity-treated fruits exhibited an increase in total phenolic content of +21.9% in S1 and +36.7% in S2 and in antioxidant capacity (+33.5% and +34.7%, for the S1 and S2 treatments, respectively). Salinity treatments registered in general higher quality parameters such as titratable acidity (+8.9 for S1 and +16.5% for S2), total soluble solids (+18.5% for S1 and +43.0% for S2) and fruit firmness (+30.7% for S1 and +60.3% for S2) in comparison with control tomato fruits. Sensory profile analysis further validated the preference for fresh consumption of tomato fruits grown with saline water. These findings suggests that salinity stress can enhance the nutritional quality and taste of the Pisanello tomato. Further investigation could explore the optimal NaCl concentration to balance tomato production and nutritional quality.