Farm records hold the static, temporal, and longitudinal details of the farms. For small-scale farming, the ability to accurately capture these records plays a critical role in formalizing and digitizing the agriculture industry. Reliable exchange of these record through a trusted platform could unlock critical and valuable insights to different stakeholders across the value chain in agriculture eco-system. Lately, there has been increasing attention on digitization of small scale farming with the objective of providing farm-level transparency, accountability, visibility, access to farm loans, etc. using these farm records. However, most solutions proposed so far have the shortcoming of providing detailed, reliable and trusted small-scale farm digitization information in real time. To address these challenges, we present a system, called Agribusiness Digital Wallet (ADW), which leverages blockchain to formalize the interactions and enable seamless data flow in small-scale farming ecosystem. Utilizing instrumentation of farm tractors, we demonstrate the ability to utilize farm activities to create trusted electronic field records (EFR) with automated valuable insights. Using ADW, we processed several thousands of small-scale farm-level activity events for which we also performed automated farm boundary detection of a number of farms in different geographies.
Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a twoweek field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of usersubmitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.
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