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.

Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings.

Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.