N. Aleixos

... In this case, the hyper-spectral images analysed using LDA also offered better results than the traditional RGB ... (2009a) used LDA to analyse hyperspectral images for ... For each image, the mean reflectance spectra from the central part were obtained and PCA was applied to the ...

This article considers how conceptual design of industrial products is supported by current CAD systems. The case of subsidiary industries, or first tier suppliers, that must simultaneously deal with different customers and CAD platforms, receive special attention. Conceptual design is critical, since the large variety of fundamental product data managed (not just geometry) would be specified, modeled and interrelated (ie functional relations), to both simplify and ensure correctness and efficiency of the next design phases of current ...

Although most of the process of canning mandarin segments is already automated, this has still not been achieved with the on-line inspection and sorting of the fruit because of the difficulty in the handling of the product and the complexity of the inspection software required to classify the segments following subjective criteria. A machine vision-based system has been developed to classify the objects that reach the line into four categories, detecting broken fruit attending, basically, to the shape of the fruit. A full working prototype has been developed for singulating, inspecting and sorting satsuma (Citrus unshiu) segments. The segments are transported over semi-transparent conveyor belts to allow illuminating the fruit from the bottom to enhance the shape of the segments against the background. The system acquires images of the segments using two cameras connected to a single computer and processes them in less than 50ms. By extracting morphological features from the objects, the system automatically identifies pieces of skin and other raw material, and separates whole segments from broken ones; it is also capable to grade between those with a slight or a large degree of breakage. Tests showed that the machine is able to correctly classify 93.2% of sound segments.

ABSTRACT Green mold (Penicillium digitatum) and blue mold (Penicillium italicum) are important sources of postharvest decay affecting the commercialization of mandarins. These fungi infections produce enormous economic losses in mandarin production if early detection is not carried out. Nowadays, this detection is performed manually in dark chambers, where the fruit is illuminated by ultraviolet light to produce fluorescence, which is potentially dangerous for humans. This paper documents a new methodology based on hyperspectral imaging and advanced machine-learning techniques (artificial neural networks and classification and regression trees) for the segmentation and classification of images of citrus free of damage and affected by green mold and blue mold. Feature selection methods are used in order to reduce the dimensionality of the hyperspectral images and determine the 10 most relevant. Neural Networks were used to segment the hyperspectral images. Results achieved using classifiers based on decision trees show an accuracy of around 93% in the problem of decay classification.

ABSTRACT The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R2 coefficient of 0.993 for size estimation and an R2 coefficient of 0.918 for the colour index.