Comparative study of the banana pulp browning process of 'Giant Dwarf' and FHIA-23 during fruit ripening based on image analysis and the polyphenol oxidase and peroxidase biochemical properties.

This work presents a novel method to associate the polyphenol oxidase (PPO) and the peroxidase (POD) activities with the ripening-mediated color changes in banana peel and pulp by computational image analysis. The method was used to follow up the de-greening of peel and browning of homogenized pulp from 'Giant Dwarf' (GD: Musa AAA, subgroup Cavendish) and FHIA-23 (tetraploid hybrid, AAAA) banana cultivars. In both cultivars, the color changes of peel during the ripening process clearly showed four stages, which were used to group the fruit into ripening stages. The PPO and POD were extracted from pulp of fruit at these ripening stages, precipitated, and partially purified by gel filtration chromatography. Moreover, the pulp browning was digitally monitored after homogenization for a span time of up to 120 min. The browning level was higher for GD than FHIA-23 tissues. This fact correlated with an 11.7-fold higher PPO activity in the GD cultivar, as compared with that of FHIA-23. POD activity was 8.1 times higher for GD as compared that that of FHIA-23.

Developing a new wireless sensor network platform and its application in precision agriculture.

Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of "smart dust" offer great advantages due to their small size, low power consumption, easy integration and support for "green" applications. Green applications are considered a hot topic in intelligent environments, ubiquitous and pervasive computing. This work evaluates a new wireless sensor network platform and its application in precision agriculture, including its embedded operating system and its routing algorithm. To validate the technological platform and the embedded operating system, two different routing strategies were compared: hierarchical and flat. Both of these routing algorithms were tested in a small-scale network applied to a watermelon field. However, we strongly believe that this technological platform can be also applied to precision agriculture because it incorporates a modified version of LORA-CBF, a wireless location-based routing algorithm that uses cluster-based flooding. Cluster-based flooding addresses the scalability concerns of wireless sensor networks, while the modified LORA-CBF routing algorithm includes a metric to monitor residual battery energy. Furthermore, results show that the modified version of LORA-CBF functions well with both the flat and hierarchical algorithms, although it functions better with the flat algorithm in a small-scale agricultural network.