Predicting the canopy conductance to water vapor of grapevines using a biophysical model in a hot and arid climate.

Canopy conductance is a crucial factor in modelling plant transpiration and is highly responsive to water stress. The objective of this study is to develop a straightforward method for estimating canopy conductance (g) in grapevines. To predict g, this study combines stomatal conductance to water vapor (g) measurements from grapevine leaves, scaled to represent the canopy size by the leaf area index (LAI), with atmospheric variables, such as net solar radiation (R) and air vapor pressure deficit (VPD).

Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling.

This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images.

Image analysis-based modelling for flower number estimation in grapevine.

Background: Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety-independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated.

vitisFlower®: Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques.

Grapevine flowering and fruit set greatly determine crop yield. This paper presents a new smartphone application for automatically counting, non-invasively and directly in the vineyard, the flower number in grapevine inflorescence photos by implementing artificial vision techniques. The application, called vitisFlower(®), firstly guides the user to appropriately take an inflorescence photo using the smartphone's camera.

Establishing the macular grading grid by means of fovea centre detection using anatomical-based and visual-based features.

This paper presents a methodology for establishing the macular grading grid in digital retinal images by means of fovea centre detection. To this effect, visual and anatomical feature-based criteria are combined with the aim of exploiting the benefits of both techniques. First, acceptable fovea centre estimation is obtained by using a priori known anatomical features with respect to the optic disc and the vascular tree.

A function for quality evaluation of retinal vessel segmentations.

Retinal blood vessel assessment plays an important role in the diagnosis of ophthalmic pathologies. The use of digital images for this purpose enables the application of a computerized approach and has fostered the development of multiple methods for automated vascular tree segmentation. Metrics based on contingency tables for binary classification have been widely used for evaluating the performance of these algorithms.

A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features.

This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts.

Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques.

Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation.