Implementations are described herein for realistic plant growth modeling and various applications thereof. In various implementations, a plurality of two-dimensional (2D) digital images that capture, over time, one or more of a particular type of plant based on one or more machine learning models to generate output, may be processed. The output may be analyzed to extract temporal features that capture change over time to one or more structural features of the particular type of plant. Based on the captured temporal features, a first parameter subspace of whole plant parameters may be learned, wherein the whole plant parameters are usable to generate a three-dimensional (3D) growth model that realistically simulates growth of the particular type of plant over time. Based on the first parameter subspace, one or more 3D growth models that simulate growth of the particular type of plant may be non-deterministically generated and used for various purposes.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.