Impact of treated wastewater on plant growth: leaf fluorescence, reflectance, and biomass-based assessment.

The study evaluated the impact of treated wastewater on plant growth through the use of hyperspectral and fluorescence-based techniques coupled with classical biomass analyses, and assessed the potential of reusing treated wastewater for irrigation without fertilizer application. Cherry tomato (Solanum lycopersicum) and cabbage (Brassica oleracea L.) were irrigated with tap water (Tap), secondary effluent (SE), and membrane effluent (ME). Maximum quantum yield of photosystem II (Fv/Fm) of tomato and cabbage was between 0.78 to 0.80 and 0.81 to 0.82, respectively, for all treatments. The performance index (PI) of Tap/SE/ME was 2.73, 2.85, and 2.48 for tomatoes and 4.25, 3.79, and 3.70 for cabbage, respectively. Both Fv/Fm and PI indicated that the treated wastewater did not have a significant adverse effect on the photosynthetic efficiency and plant vitality of the crops. Hyperspectral analysis showed higher chlorophyll and nitrogen content in leaves of recycled water-irrigated crops than tap water-irrigated crops. SE had 10.5% dry matter composition (tomato) and Tap had 10.7% (cabbage). Total leaf count of Tap/SE/ME was 86, 111, and 102 for tomato and 37, 40, and 42 for cabbage, respectively. In this study, the use of treated wastewater did not induce any photosynthetic-related or abiotic stress on the crops; instead, it promoted crop growth.

Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field.

Soil organic content (SOC), an indicator of soil fertility, can be estimated quickly and accurately with remote sensing (RS) datasets; however, the issue of vegetation cover on the field still remains a major concern. In order to minimize the effects of vegetation cover, studies relating reflectance spectra to SOC may require bare soil. However, acquiring satellite images devoid of vegetation is still an enormous challenge for RS techniques. This is because the area that may have been accurately predicted at a targeted date is sometimes limited since many pixels are covered by vegetation. The study goal was to assess the impact of using UAV-borne imagery coupled with auxiliary datasets, which include spectral indices (SPIs) and terrain attributes (TAs) (at 20 cm and 30 m resolution), singly or merged, to estimate and map SOC in an erosion-prone agricultural field. Both field samples and UAV imagery were acquired while the fields were bare. Using a grid sampling design, 133 soil surface samples were collected. The models used include partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), and regularised random forest (RFF). The models were evaluated using the root mean squared error (RMSE), the coefficient of determination (R2), ratio of performance to interquartile distance (RPIQ), and the mean absolute error (MAE). For prediction, the three merged datasets (R2val = 0.86, RMSEval = 0.13, MAEval = 0.11, RPIQval = 4.19) outperformed the best separate dataset (R2val = 0.82, RMSEval = 0.15, MAEval = 0.10, RPIQval = 2.08). Though all datasets detected both low and high estimates of soil SOC, the three merged datasets with EGB showed a less extreme prediction error. This study demonstrated that SOC can be estimated with high accuracy using completely bare soil UAV imagery with other auxiliary data, and it is thus highly recommended.