Design and development of machine learning and data optimization processes for detection of biogenic patterns

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EIS is working on the development of a wireless plant health monitoring system that is capable of gathering information from crops through a series of sensory systems that monitor and interpret different plant signal patterns. This approach constitutes of three main steps: (1) Identifying and refining plant-driven measurable indicators; (2) designing, and customizing high-resolution sensory systems that is deployable in various agricultural environment (e.g., greenhouses and vineyards); (3) developing interpretation algorithms and reporting software; and (4) designing wireless machine-to-machine (M2M) solutions for non-invasive implementation of the technology. The underlying challenges of such technology are the infrastructure to handle large amount of data, advanced processing and data analytics and safe keeping and encryption. The main focus of this project will be on the third step. In collaboration with EIS who has already developed some interpretation algorithms and models, we will review and assess the nature of the sensory data and develop novel data optimization and machine learning processes. The objectives are to (i) develop a data reduction pipeline to obtain an optimal and interpretable subset of the sensory data suitable for predicting plant infestation; (ii) develop an algorithm for constructing a predictive model that uses sensory information to assign a given plant in an agricultural environment an infection status; (iii) should time permit, design a set of processes that can transfer information from a given agricultural environment to establish a classification model for another agricultural environment for real time use.