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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.

Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice., Comment: 5 pages, 4 figures, IEEE Global Conference on Signal and Information Processing (GlobalSIP 2019)