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Increased expression of the aquaporin NtAQP1, which is known to function as a plasmalemma channel for CO₂ and water, increases the rate of both photosynthesis and transpiration. In contrast, increased expression of Arabidopsis hexokinase1 (AtHXK1), a dual-function enzyme that mediates sugar sensing, decreases the expression of photosynthetic genes and the rate of transpiration and inhibits growth. Here, we show that AtHXK1 also decreases root and stem hydraulic conductivity and leaf mesophyll CO₂ conductance (g(m)). Due to their opposite effects on plant development and physiology, we examined the relationship between NtAQP1 and AtHXK1 at the whole-plant level using transgenic tomato plants expressing both genes simultaneously. NtAQP1 significantly improved growth and increased the transpiration rates of AtHXK1-expressing plants. Reciprocal grafting experiments indicated that this complementation occurs when both genes are expressed simultaneously in the shoot. Yet, NtAQP1 had only a marginal effect on the hydraulic conductivity of the double-transgenic plants, suggesting that the complementary effect of NtAQP1 is unrelated to shoot water transport. Rather, NtAQP1 significantly increased leaf mesophyll CO₂ conductance and enhanced the rate of photosynthesis, suggesting that NtAQP1 facilitated the growth of the double-transgenic plants by enhancing mesophyll conductance of CO₂.

Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management. [ABSTRACT FROM AUTHOR]

Precision fertilization is crucial for sustainable and efficient world farming. However, traditional chemical mineral analyses are costly and time-consuming, which limits the amount of data that can be collected and the frequency of updates to fertilization programs. Hence, we set to restructure mineral diagnostics in agriculture. First, we developed a unique experimental setup and produced ∼2000 leaf samples at a wide concentration range of macronutrients (0.5–10 mg P per g, 1–50 mg K per g, or 2–40 mg Ca per g) and trace elements (0.05–0.4 mg Mn per g, 0.05–0.4 mg Fe per g, or 0.005–0.06 mg Zn per g) in multiple crops. We scanned the foliar samples by X-ray fluorescence and determined that emissions at the 1–10 keV range resonate with macronutrients and trace elements in plants. While initial correlations for trace elements were unsatisfactory for trace elements (R2 < 0.7), we applied a range of statistical tools to support chemical spectroscopy for the mineral matrices in leaves. The chemometric investigation developed into crop-specific predictive models that fitted chemical analyses of mineral concentrations by R2s of ∼0.9 and an RMSE of 5% of the concentration range. We also developed a single model for all the crops but concluded that it often critically reduced the accuracy (a fourfold increase in RMSE) of the mineral analyses. Nonetheless, the XRF-PLSR approach is rapid and significantly more cost effective than traditional laboratory essays. Thus, developing models for numerous crops is feasible, and XRF tools could integrate mineral analyses in decision support tools for farming. [ABSTRACT FROM AUTHOR]