利用WIWAM高光譜成像技術(shù)對植物生理特性進行無損分析

 
WIWAM植物表型成像系統(tǒng)由比利時SMO公司與Ghent大學VIB研究所研制生產(chǎn)，整合了LED植物智能培養(yǎng)、自動化控制系統(tǒng)、葉綠素熒光成像模塊、植物熱成像模塊、植物近紅外成像模塊、植物高光譜模塊、植物多光譜模塊、植物CT斷層掃描分析模塊、自動條碼識別管理、RGB真彩3D成像等多項先進技術(shù)，以優(yōu)化的方式實現(xiàn)大量植物樣品——從擬南芥、小麥、水稻、玉米、大豆到各種其它植物的生理生態(tài)與形態(tài)結(jié)構(gòu)表型成像分析，高通量植物表型成像分析測量、植 物脅迫響應(yīng)成像分析測量、植物生長分析測量、生態(tài)毒理學研究、性狀識別及植物生理生態(tài)分析研究等。

高光譜圖像分析，用于植物早期應(yīng)激癥狀的非破壞性視覺成像；
使用機器學習回歸算法開發(fā)與水分脅迫相關(guān)的生理性狀預測模型；
開發(fā)一種數(shù)據(jù)驅(qū)動的光譜分析方法，從植物的正常生長動態(tài)中量化與脅迫相關(guān)的現(xiàn)象；

在高通量植物表型分析平臺環(huán)境下，通過對玉米植株水分脅迫的小規(guī)模研究，驗證了數(shù)據(jù)驅(qū)動的方法
獲取植物生理特性的傳統(tǒng)方法是基于通過生化提取或剪葉的破壞性測量，從而限制了通量。隨著高光譜成像傳感器的發(fā)展，快速、無創(chuàng)、無損地測量植物的生理狀態(tài)成為可能。在這項工作中，提出了一種從高光譜圖像表征植物狀態(tài)的非破壞性方法。提出了一種基于機器學習回歸（MLR）算法的有監(jiān)督數(shù)據(jù)驅(qū)動方法，用于生成四個目標生理性狀的預測模型：水勢、光系統(tǒng)II的有效量子產(chǎn)量、蒸騰速率和氣孔導度。標準正態(tài)變量（SNV）轉(zhuǎn)換反射光譜被用作建立回歸模型的輸入變量。研究了三種MLR算法：高斯過程回歸（GPR）、核嶺回歸（KRR）和偏最小二乘回歸（PLSR）作為建立目標生理性狀預測模型的候選方法。驗證結(jié)果表明，基于探地雷達算法開發(fā)的非線性預測模型對所有植物性狀的估計精度最佳。將最佳預測模型應(yīng)用于小型表型試驗，以研究玉米植株的干旱脅迫響應(yīng)。結(jié)果表明，早在干旱誘導3天后，干旱脅迫下的植株與正常生長動態(tài)下的植株的所有估計性狀都存在顯著差異。 




Highlights
The analysis of hyperspectral images for non-destructive visual mapping of early stress symptoms in plants.
The use of Machine Learning Regression algorithms to develop prediction models for water-stress related physiological traits.
The development of a data-driven spectral analysis method to quantify stress-related phenomena from regular growth dynamics in plants.
The validation of the data-driven method by a small-scale study of water-stress of maize plants in a high-throughput plant phenotyping platform setting 

Abstract
Conventional methods to access plant physiological traits are based on destructive measurements by means of biochemical extraction or leaf clipping, thereby limiting the throughput capability. With advances in hyperspectral imaging sensor, fast, non-invasive and non-destructive measurements of a plant’s physiological status became feasible. In this work, a non-destructive method for the characterization of a plant’s status from hyperspectral images is presented. A supervised data-driven method based on Machine Learning Regression (MLR) algorithms was developed to generate prediction models of four targeted physiological traits: water potential, effective quantum yield of photosystem II, transpiration rate and stomatal conductance. Standard Normal Variate (SNV) transformed reflectance spectra were used as the input variables for building the regression model. Three MLR algorithms: Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), and Partial Least Squares Regression (PLSR) were explored as candidate methods for building the prediction model of the targeted physiological traits. Validation results show that the non-linear prediction models, developed based on the GPR algorithm produced the best estimation accuracy on all plant traits. The best prediction models were applied to a small-scale phenotyping experiment to study drought stress responses in maize plants. Results show that all estimated traits revealed a significant difference between plants under drought stress and normal growth dynamics as early as after 3 days of drought induction.