Automatic Measurement of Morphological Traits of Typical Leaf Samples

Abstract

It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

“…Typically, these characteristics vary across a wide range of values for plant point clouds, and a threshold that works for one plant type or organ may not be appropriate for another. To address this, Huang et al [205] developed a multi-level region-growing segmentation to find a suitable adaptive segmentation scale for different input data. They applied the proposed method to perform individual leaf segmentation of two leaf shape models with different levels of occlusion.…”

“…Aubergine [252] Bamboo-leaf oak tree [252] Barley [30,156] Benth (Nicotiana benthamiana) [183] Birch [174] Botanic trees [168] Cereal plants [27] Chickpea [328] Elm tree [182] Grape [329] Grapevine [35] Horse Chestnut [174] Japanese cedar [252] Japanese larch [252] Maize (Corn) [34,129,160,186,330,331] Orchard tree [175] Poplar [174] Rapeseed (Brassica sp.) [32] Red Oak [174] Rosebush [264] Sorghum [328] Soybean [25] Sugar beet [25,36] Sugar maple [182] Sweet Chestnut [174] Thale cress (Arabidopsis) [10,28,156] Tomato [34,160,183] Wheat [31,35,36] Yellow birch [182] Others [205,332] Measures accurately the distance between the sensor and a target based on the elapsed time between the emission and return of laser pulses ('Time-of-Flight' (ToF) method) or based on trigonometry (the 'optical probe' or 'light section' methods).…”

“…Random Sample Consensus (RANSAC) Grape [329] Maize (Corn) [82] Soybean [77,170] Other [135,205] Fits geometric primitives to point clouds by choosing the best among fits to numerous random samplings of the data [128].…”