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Abstract. This work presents an approach focused in enhancing the quality of tomato crops. We are developing and using low cost computational strategies to support early detection of the late blight. Our approach consorts tomatoes cultivars in an experimental field with inexpensive computer-aided resources based on Web and Android mobile tools in which workers collect scouting data and annotations and take images about the state of the crop, and in image filtering techniques and pattern recognition to detect foliage diseases on tomatoes images. In this study, we use provenance metadata about field observations, images and farmers' annotations as well, to improve the efficiency and accuracy of the patterns recognition algorithms. Our identification method achieved a hit rate of 94.12 %, using a reduced set of digital images of the tomato crops.

Degenerative neurological diseases, although common in the population, are difficult to diagnose. However, it is known that most of them are directly related to the so-called oxidative stress. Understanding and evaluating how this process takes place is of great interest and, in this sense, this work extends the existing mathematical and computational models for the oxidative stress process (REIS, 2005; REIS et al., 2006; VIANNA, 2005; VIANNA; REIS; CARVALHO, 2012), incorporating an aspect not previously evaluated, the influence of homocysteine indices on the oxidants present in this process. The results indicate that hyperhomocysteinemia can in fact cause oxidative stress and consequent neuronal death, leading to the appearance of neurodegenerative diseases.