Machine Learning for High-Throughput Stress Phenotyping in Plants

Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits. 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.