Machine Learning for High-Throughput Stress Phenotyping in Plants Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar  Trends in Plant Science  Volume 21, Issue 2, Pages 110-124 (February 2016) DOI: 10.1016/j.tplants.2015.10.015 Copyright © 2015 The Authors Terms and Conditions

Figure 1 Key Figure: Machine Learning (ML) Tools for High-Throughput Stress Phenotyping (A) High-throughput stress phenotyping in soybean field at various growth stages and at different heights using aircraft, UAV, and UGV. (B) Identification, classification, quantification, and prediction (ICQP) of plant diseases in soybean. (C) ML algorithms used in ICQP of plant stresses. (D) Classification of ML algorithms into generative and discriminative. Abbreviations: ANN, artificial neural network; BC, Bayes classifier; BN, Bayesian network; BM, Boltzmann machine; CRF, conditional random field; CNN, convolutional neural network; DT, decision tree; DNN, deep neural network; GMM, Gaussian mixture models; GP, Gaussian process; HMM, hidden Markov model; HC, hierarchical clustering, ICA, independent component analysis; K-MC, K-means clustering; K-NN, k-nearest neighbor classifier; Lat DA, latent Dirichlet allocation, LDA, linear discriminant analysis; Lin R, linear regression; LR, logistic regression; MF, matrix factorization; NB, naïve Bayes; NLR, nonlinear regression; PCA, principal component analysis; RF, random forests; SOM, self-organizing map; SVM, support vector machine; UAV, unmanned aerial vehicle; UGV, unmanned ground vehicle. Trends in Plant Science 2016 21, 110-124DOI: (10.1016/j.tplants.2015.10.015) Copyright © 2015 The Authors Terms and Conditions