FACT-AI: BIG DATA-ENABLED REAL-TIME LEARNING AND DECISION MAKING FOR FIELD-BASED HIGH THROUGHPUT PLANT PHENOTYPING

The overarching goal of the proposed research is the design and implementation of a scalable high-throughput plant phenotyping (HTPP) system byharnessing potentials of big data analytics and real-time decision making. Thespecific objectivesof this project are as follows: (1)design and validate efficient data acquisition systems and algorithms for plant phenotypic traitmeasurements using static sensors, as well as mobile robots equipped with advanced sensors; (2) develop data processing, reduction, storage, and real-time analytic algorithms using distributedcomputing tools (on the fogs) in order to only transfer useful data; (3) design computationally fast (and real-time) deep learning-based algorithms by exploiting big data storage anddistributed processing tools to extract/analyze phenotypic traits from diverse sources of data; (4) design an interface to visualize real-time and historical data analysis results and spatio-temporaldata; (5) implement and validate the big data pipeline and its multiple components for two case studies:cotton bloom detection/counting and water stress analysis. While the former is addressed using state-of-the-art multi-object detection techniquescombined with color segmentation and image transformation methods, the latter will usedeep learning-based classificationmethods.