High-throughput phenotyping (HTP) has unlocked new prospects for non-destructive field-based phenotyping. Autonomous, semi-autonomous, or manual platforms equipped with single or multiple sensors collect spatial and temporal data, resulting in massive amounts of data for analysis and storage.
The enormous volume, variety, and velocity of HTP data generated by such platforms make it a ‘big data’ problem. Big data generated by these near real-time platforms must be efficiently archived and retrieved for analysis. The analysis and interpretation of these large datasets is quite challenging.
Sophisticated data collection, storage, and processing are becoming ubiquitous, and newer areas of application are emerging constantly. One such relatively new domain is plant stress analytics.
ML algorithms are a very promising approach for faster, more efficient, and better data analytics. ML being inherently multidisciplinary draws inspiration and utilizes concepts from probability theory, statistics, decision theory, optimization, and visualization.
Most current applications of ML tools in plant sciences have focused on using a limited set of ML tools (SVM, ANN). A good understanding of which, when, and how various ML tools can be applied will be very informative to the plant community to leverage these ML tools.