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The xanthophyll cycle regulates the energy flow to photosynthetic reaction centres of plant leaves. Changes in the de-epoxidation state (DEPS) of xanthophyll cycle pigments can be observed as changes in the leaf absorption of light with wavelengths between 500 to 570 nm. These spectral changes can be a good remote sensing indicator of the photosynthetic efficiency, and are traditionally quantified with a twoband physiologically based optical index, the Photochemical Reflectance Index (PRI). In this paper, we present an extension of the plant leaf radiative transfer model Fluspect (Fluspect-CX) that reproduces the spectral changes in a wide band of green reflectance: a radiative transfer analogy to the PRI. The idea of Fluspect-CX is to use in vivo specific absorption coefficients for two extreme states of carotenoids, representing the two extremes of the xanthophyll de-epoxidation, and to describe the intermediate states as a linear mixture of these two states. The 'photochemical reflectance parameter' (Cx) quantifies the relative proportion of the two states. Fluspect-CX simulates leaf chlorophyll fluorescence (ChlF) excitation-emission matrices, as well as reflectance (R) and transmittance (T) spectra as a function of leaf structure, pigment contents and Cx. We describe the calibration of the model and test its performance using various experimental datasets. Furthermore, we retrieved Cx from optical measurements of various datasets. The retrieved Cx correlates well with xanthophyll DEPS (R2=0.57), as well with non-photochemical quenching (NPQ) of fluorescence (R2=0.78). The correlation with NPQ enabled us to incorporate Fluspect-CX in the model SCOPE to scale the processes to the canopy level. Introducing the dynamic green reflectance into a radiative transfer model provides new means to study chlorophyll fluorescence and PRI dynamics on leaf and canopy scales, which is crucial for the remote sensing.

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Summary In photosynthesis models following the Farquhar formulation, the maximum carboxylation rate V cmax is the key parameter. Remote‐sensing indicators, such as reflectance ρ and Chl fluorescence (ChlF), have been proven as valuable estimators of photosynthetic capacity and can be used as a constraint to V cmax estimation. We present a methodology to retrieve V cmax from leaf ρ and ChlF by coupling a radiative transfer model, fluspect , to a model for photosynthesis. We test its performance against a unique dataset, with combined leaf spectral, gas exchange and pulse‐amplitude‐modulated measurements. Our results show that the method can estimate the magnitude of V cmax estimated from the far‐red peak of ChlF and green ρ or transmittance τ , with values of root‐mean‐square error below 10 μmol CO 2 m −2 s −1 . At the leaf level, the method could be used for detection of plant stress and tested against more extensive datasets. With a similar scheme devised for the higher spatial scales, such models could provide a comprehensive method to estimate the actual photosynthetic capacity of vegetation.

Purpose of Review A short introduction to the spectral imaging (SI) of plants along with a comprehensive overview of the recent research works related to disease detection in plants using autonomous phenotyping platforms is provided. Key benefits and challenges of SI for plant disease detection on robotic platforms are highlighted. Recent Findings SI is becoming a potential tool for autonomous platforms for non-destructive plant assessment. This is because it can provide information on the plant pigments such as chlorophylls, anthocyanins and carotenoids and supports quantification of biochemical parameters such as sugars, proteins, different nutrients, water and fat content. A plant suffering from diseases will exhibit different physicochemical parameters compared with a healthy plant, allowing the SI to capture those differences as a function of reflected or absorbed light. Summary Potential of SI to non-destructively capture physicochemical parameters in plants makes it a key technique to support disease detection on autonomous platforms. SI can be broadly used for crop disease detection by quantification of physicochemical changes in the plants.