Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform

The potential of close-range hyperspectral imaging (HSI) as a tool for detecting early drought stress responses in plants grown in a high-throughput plant phenotyping platform (HTPPP) was explored. Reflectance spectra from leaves in close-range imaging are highly influenced by plant geometry and its specific alignment towards the imaging system. This induces high uninformative variability in the recorded signals, whereas the spectral signature informing on plant biological traits remains undisclosed. A linear reflectance model that describes the effect of the distance and orientation of each pixel of a plant with respect to the imaging system was applied. By solving this model for the linear coefficients, the spectra were corrected for the uninformative illumination effects. This approach, however, was constrained by the requirement of a reference spectrum, which was difficult to obtain. As an alternative, the standard normal variate (SNV) normalisation method was applied to reduce this uninformative variability. Once the envisioned illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To distinguish the stress-related phenomena from regular growth dynamics, a spectral analysis procedure was developed based on clustering, a supervised band selection, and a direct calculation of a spectral similarity measure against a reference. To test the significance of the discrimination between healthy and stressed plants, a statistical test was conducted using a one-way analysis of variance (ANOVA) technique. The proposed analysis techniques was validated with HSI data of maize plants (Zea mays L.) acquired in a HTPPP for early detection of drought stress in maize plant. Results showed that the pre-processing of reflectance spectra with the SNV effectively reduces the variability due to the expected illumination effects. The proposed spectral analysis method on the normalized spectra successfully detected drought stress from the third day of drought induction, confirming the potential of HSI for drought stress detection studies and further supporting its adoption in HTPPP

Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution

Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging

While Hyperspectral Imaging (HSI) has been successfully applied for remote monitoring of vegetation, its use is still underdeveloped in close range settings, where a higher spatial and temporal resolution is applied to measure functional plant traits. Much more than remotely, leaf reflectance spectra in close range are very sensitive to plant geometry and specific alignment of the imaging system. In particular, the spectrum of each plant pixel heavily depends on its distance and inclination towards the light source and sensor. To deal with these effects, this work studies the influence of illumination and plant geometry on the recorded HSI in a specific indoor setup (PHENOVISION at VIB, Ghent, Belgium). Based on simple optical models, the reflectance spectra are modeled using multivariate linear regression. The obtained model coefficients are then used to correct the spectra. Finally, a commonly applied scatter correction method, the Standard Normal Variate (SNV) transformation is shown to remove the illumination and geometry effects

Waste treatment company decision-making in a complex system of markets influenced by the circular economy

As waste treatment companies are pivotal for a shift towards the circular economy, more insight into their decision-making process is needed. This paper explores how waste treatment companies make decisions facing the circular economy by using a qualitative research approach (semi-structured interviews, workshop, focus group) with 10 companies and experts across the EU in a framework of four interrelated markets. We found that the circular economy will exacerbate competition across markets, which creates difficulties for both public and private waste treatment companies to make the shift towards the circular economy in a changing technology landscape. Furthermore, there are institutional contradictions that arise due to lagging support mechanisms, leaving waste treatment companies with significant uncertainties concerning their transfer to a circular economy. Therefore, waste treatment companies will require a regulatory framework that meets their needs across various markets

Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform

The study of physiological processes resulting from water-limited conditions in crops is essential for the selection of drought-tolerant genotypes and the functional analysis of related genes. A promising, non-invasive technique for plant trait analysis is close-range hyperspectral imaging (HSI), which has great potential for the early detection of plant responses to water deficit stress. In this work, a data analysis method is described that, unlike vegetation indices, the present method applies spectral similarity on selected bands with high discriminative information, while requiring a careful treatment of uninformative illumination effects. The latter issue is solved by a standard normal variate (SNV) normalization that removes linear effects and a supervised clustering approach to remove pixels that exhibit nonlinear multiple scattering effects. On the remaining pixels, the stress-related dynamics is quantified by a spectral analysis procedure that involves a supervised band selection procedure and a spectral similarity measure against well-watered control plants. The proposed method was validated by a large-scale study of water-stress and recovery of maize plants in a high-throughput plant phenotyping platform. The results showed that the analysis method allows for an early detection of drought stress responses and of recovery effects shortly after re-watering