Early drought stress detection in cereals: simplex volume maximization for hyperspectral image analysis

Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisation technique, simplex volume maximisation (SiVM), to hyperspectral data. It is an unsupervised classification approach, optimised for fast computation of massive datasets. It allows calculation of how similar each spectrum is to observed typical spectra. This provides the means to express how likely it is that one plant is suffering from stress. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter experiment and to agricultural corn plots subjected to a two factorial field setup altering water and nutrient availability. Both experiments were conducted on the canopy level. SiVM was significantly better than using a combination of established vegetation indices. In the corn plots, SiVM clearly separated the different treatments, even though the effects on leaf and canopy traits were subtle

Intercomparison of NO, NO2, NOy, and ROx measurements during the Oxidizing capacity of the trospospheric atmosphere campaign 1993 at Izana

An informal comparison of NO, NO2, NO y , O3, and RO x measurements obtained by different instruments and techniques at Izaña in 1993 during the European Oxidizing Capacity of the Tropospheric Atmosphere (OCTA) campaign was performed. For O3, two UV instruments agree within 7% (95% cl.) limited by a difference in response of 7.0%±0.2% (95% cl.) which likely was caused by O3 losses in one of the inlet lines. The NO mixing ratios obtained by two NO/O3 Chemiluminescence (CL) instruments range between 0–200 parts per trillion by volume (pptv), except for short periods influenced by traffic pollution. The response of the two CL detectors agrees within 3%±10% (95% cl.). The NO y data, ranging between 100 pptv and several ppbv in plumes, were obtained using two different gold‐CO‐converters and inlet designs with subsequent CL detection of NO. A systematic difference in the slope between the two data series of 1.44±0.05 (95% cl.) was likely caused by NO y , losses in the inlet line of one of the instruments. Three different NO2 data sets were obtained using Tunable Diode Laser Absorption Spectroscopy (TDLAS), a photolytic converter/CL technique (PLC/CL), and the Matrix Isolation Electron Spin Resonance (MIESR) technique. The linear slopes between the data sets of the three methods are consistent with unity at a 95% confidence level, 1.13±0.30 (TDL versus PLC/CL), 0.90±0.47 (TDL versus MIESR), and 1.04+0.34 (PLC/CL versus MIESR). RO x measurements were performed by three different chemical amplifier (CA) designs and the MIESR technique. Using 30‐min averaged values between 13–65 pptv, two CA instruments agree within 25% (95% cl.) with the mean of MIESR (1.01+0.20 and 0.98±0.24, 95% cl.), while the third CA responded low (0.65±0.32, 95% cl.)

Unsupervised domain adaptation for early detection of drought stress in hyperspectral images

Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible