Performance of vertical and horizontal treatment wetlands planted with ornamental plants in Central Chile: comparative analysis of initial operation stage for effluent reuse in agriculture.

This study comparatively evaluated effluent reuse from two TWs-a horizontal subsurface flow (HF) and a vertical subsurface flow (VF)-used for rural wastewater treatment in Central Chile during the initial operation stage. The two TWs were planted with Zantedeschia aethiopica and were operated for 10 months at a pilot scale. The water quality of the influent and effluents was measured and compared with reuse regulations.

Variation of the feeding/resting period in modified vertical treatment wetlands (depth, zeolite as medium) employed for treating rural domestic wastewater in tourist areas.

This work aimed to evaluate the performance of modified vertical flow treatment wetlands (VF-TWs) in terms of depth and medium to assess the effect of the feeding/resting periods and footprint (FP). The modifications were proposed for treating domestic wastewater in rural areas with flow variations such as tourist sites. The experimental setup included six laboratory-scale VF-TWs: (a) normal (VF-N), bed depth 1.

Evaluation of Bed Depth Reduction, Media Change, and Partial Saturation as Combined Strategies to Modify in Vertical Treatment Wetlands.

The aim of this work was to evaluate the performance of vertical subsurface flow treatment wetlands (VSSF TWs) for treating rural domestic wastewater when strategies such as bed depth reduction and media change are used in combination with bottom saturation. Two treatment wetland systems were implemented: normal (VF-N), with a bed depth of 1.0 m, and modified (VF-M), with a bed depth of 0.

Grapevine yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions.

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them.