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In high-value sweet cherry (Prunus avium), the red coloration - determined by the anthocyanins content - is correlated with the fruit ripeness stage and market value. Non-destructive spectroscopy has been introduced in practice and may be utilized as a tool to assess the fruit pigments in the supply chain processes. From the fruit spectrum in the visible (Vis) wavelength range, the pigment contents are analyzed separately at their specific absorbance wavelengths.
A drawback of the method is the need for re-calibration due to varying optical properties of the fruit tissue. In order to correct for the scattering differences, most often the spectral intensity in the visible spectrum is normalized by wavelengths in the near infrared (NIR) range, or pre-processing methods are applied in multivariate calibrations.
In the present study, the influence of the fruit scattering properties on the Vis/NIR fruit spectrum were corrected by the effective pathlength in the fruit tissue obtained from time-resolved readings of the distribution of time-of-flight (DTOF). Pigment analysis was carried out according to Lambert-Beer law, considering fruit spectral intensities, effective pathlength, and refractive index. Results were compared to commonly applied linear color and multivariate partial least squares (PLS) regression analysis. The approaches were validated on fruits at different ripeness stages, providing variation in the scattering coefficient and refractive index exceeding the calibration sample set.
In the validation, the measuring uncertainty of non-destructively analyzing fruits with Vis/NIR spectra by means of PLS or Lambert-Beer in comparison with combined application of Vis/NIR spectroscopy and DTOF measurements showed a dramatic bias reduction as well as enhanced coefficients of determination when using both, the spectral intensities and apparent information on the scattering influence by means of DTOF readings. Corrections for the refractive index did not render improved results.
Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany - the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral im-agery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for vari-ous disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b).
Decoupling of optical properties appears challenging, but vital to get better insight of the relationship between light and fruit attributes. In this study, nine solid phantoms capturing the ranges of absorption (μa) and reduced scattering (μs’) coefficients in fruit were analysed non-destructively using laser-induced backscattering imaging (LLBI) at 1060 nm. Data analysis of LLBI was carried out on the diffuse reflectance, attenuation profile obtained by means of Farrell’s diffusion theory either calculating μa [cm−1] and μs’ [cm−1] in one fitting step or fitting only one optical variable and providing the other one from a destructive analysis. The nondestructive approach was approved when calculating one unknown coefficient non-destructively, while no ability of the method was found to analysis both, μa and μs’, non-destructively. Setting μs’ according to destructive photon density wave (PDW) spectroscopy and fitting μa resulted in root mean square error (rmse) of 18.7% in comparison to fitting μs’ resulting in rmse of 2.6%, pointing to decreased measuring uncertainty, when the highly variable μa was known.
The approach was tested on European pear, utilizing destructive PDW spectroscopy for setting one variable, while LLBI was applied for calculating the remaining coefficient. Results indicated that the optical properties of pear obtained from PDW spectroscopy as well as LLBI changed concurrently in correspondence to water content mainly. A destructive batch-wise analysis of μs’ and online analysis of μa may be considered in future developments for improved fruit sorting results, when considering fruit with high variability of μs’.
Quality attributes of fruit determine its acceptability by the retailer and consumer. The objective of this work was to investigate the potential of absorption (μa) and reduced scattering (μs’) coefficients of European pear to analyze its fruit flesh firmness and soluble solids content (SSC). The absolute reference values, μa* (cm−1) and μs’* (cm−1), of pear were invasively measured, employing multi-spectral photon density wave (PDW) spectroscopy at preselected wavelengths of 515, 690, and 940 nm considering two batches of unripe and overripe fruit. On eight measuring dates during fruit development, μa and μs’ were analyzed non-destructively by means of laser light backscattering imaging (LLBI) at similar wavelengths of 532, 660, and 830 nm by means of fitting according to Farrell’s diffusion theory, using fix reference values of either μa* or μs’*. Both, the μa* and the μa as well as μs’* and μs’ showed similar trends. Considering the non-destructively measured data during fruit development, μa at 660 nm decreased 91 till 141 days after full bloom (dafb) from 1.49 cm−1 to 0.74 cm−1 due to chlorophyll degradation. At 830 nm, μa only slightly decreased from 0.41 cm−1 to 0.35 cm−1. The μs’ at all wavelengths revealed a decreasing trend as the fruit developed. The difference measured at 532 nm was most pronounced decreasing from 24 cm−1 to 10 cm−1, while at 660 nm and 830 nm values decreased from 15 cm−1 to 13 cm−1 and from 10 cm−1 to 8 cm−1, respectively. When building calibration models with partial least-squares regression analysis on the optical properties for non-destructive analysis of the fruit SSC, μa at 532 nm and 830 nm resulted in a correlation coefficient of R = 0.66, however, showing high measuring uncertainty. The combination of all three wavelengths gave an enhanced, encouraging R = 0.89 for firmness analysis using μs’ in the freshly picked fruit.
Precision fruticulture addresses site or tree-adapted crop management. In the present study, soil and tree status, as well as fruit quality at harvest were analysed in a commercial apple (Malus × domestica 'Gala Brookfield'/Pajam1) orchard in a temperate climate. Trees were irrigated in addition to precipitation. Three irrigation levels (0, 50 and 100%) were applied. Measurements included readings of apparent electrical conductivity of soil (ECa), stem water potential, canopy temperature obtained by infrared camera, and canopy volume estimated by LiDAR and RGB colour imaging. Laboratory analyses of 6 trees per treatment were done on fruit considering the pigment contents and quality parameters. Midday stem water potential (SWP), normalized crop water stress index (CWSI) calculated from thermal data, and fruit yield and quality at harvest were analysed. Spatial patterns of the variability of tree water status were estimated by CWSI imaging supported by SWP readings. CWSI ranged from 0.1 to 0.7 indicating high variability due to irrigation and precipitation. Canopy volume data were less variable. Soil ECa appeared homogeneous in the range of 0 to 4 mS m-1. Fruit harvested in a drought stress zone showed enhanced portion of pheophytin in the chlorophyll pool. Irrigation affected soluble solids content and, hence, the quality of fruit. Overall, results highlighted that spatial variation in orchards can be found even if marginal variability of soil properties can be assumed.