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Environment and agriculture related applications have been gaining ground for the past several years and have been the
context for researches in ubiquitous and pervasive computing. This study is a part of a bigger study that uses artificial
intelligence in developing models to detect, monitor, and forecast the spread of Fusarium oxysporum cubense TR4 (FOC
TR4) on Cavendish bananas cultivated in the Philippines. To implement an Intelligent Farming system, 1) wireless
sensor nodes (WSNs) are deployed in Philippine banana plantations to collect soil parameter data that is considered to
affect the health of Cavendish bananas, 2) a custom built smartphone application is used for collecting, storing, and
transmitting soil data, plant images and plant status data to a cloud storage, and 3) a custom built web application is used
to load and display results of physico-chemical analysis of soil, analysis of data models, and geographic locations of
plants being monitored. This study discusses the issues, considerations, and solutions implemented in the development of
an asynchronous communication channel to ensure that all data collected by WSNs and smartphone applications are
transmitted with a high degree of accuracy and reliability. From a design standpoint: standard API documentation on
usage of data type is required to avoid inconsistencies in parameter passing. From a technical standpoint, there is a need
to include error-handling mechanisms especially for delays in transmission of data as well as generalize method of
parsing thru multidimensional array of data. Strategies are presented in the paper.
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