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Portable hyperspectral imaging was used for field and indoor spectra acquisition of the strawberries at three ripeness stages: ripe, mid-ripe and unripe. The mean spectra were pre-processed by multiplicative scatter correction (MSC). Principal component analysis (PCA) was employed to generate score scatter plots and visualize score images for differentiating specific grouping of samples. Three methods, including X-loading weight, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were applied to extract the effective wavelengths. Two classifiers, partial least squares – discriminant analysis (PLS-DA) and least squares – support vector machine (LS-SVM) were used for ripeness assessment. The results showed that the overall accuracy of all classifiers for field assessment ranged from 91.7% to 96.7%, slightly lower than for indoor assessment. Furthermore, the LS-SVM model combined with effective wavelengths with the CARS method performed better for field assessment of strawberry ripeness, providing an accuracy of 96.7%. It can be concluded that hyperspectral imaging can be used for real-time assessment of strawberry ripeness in the field. [ABSTRACT FROM AUTHOR]

• Assessed was the blueberry buds damage due to lab-simulated freezing events. • Buds were imaged at bud swell and early pink stages in 2017 and 2018 season. • Salient wavelengths were selected using principal component analysis and successive projection algorithm technique. • Partial least squares discriminant analysis classifier evaluated the robustness of salient wavelengths. The floral tissue of blueberry buds is very sensitive to the freezing and can be damaged during early spring freeze events. Such damage results in loss of yield to the commercial blueberry producers. Therefore, this study investigated the feasibility of injured blueberry buds damage detection during early spring freeze using hyperspectral imaging (HSI) technique. Imaged were the buds from bud swell and early pink stage for two growing seasons. Prior to imaging, buds in 2017 were subjected to freezing temperatures of −7 to −16 °C and −2 to −11 °C at respective growth stages. In 2018 season, buds were frozen at −11 to −20 °C and −4 to −13 °C at two respective sampling date for bud swell stage and at −2 to −11 °C for early pink growth stage. The HSI data was obtained using a push broom type system in the spectral wavelength ranges of 517–1729 nm. For the imaged buds, each of the indivisual buds on sampled shoot was considered as one sample, and the average spectra of such bud within 540 to 1599 nm was extracted for further analysis. Principal component analysis and successive projection algorithms were applied to select key wavelengths related to bud damage symptoms. Common wavelengths of 615, 673, 690, 756, 979 and 1467 nm were identified for both the growth stages. Partial least squares discriminant analysis was performed to identify normal and injured bud samples with the selected common wavelengths. Performance of the models manifested good results with better sensitivity (>0.80), specificity (>0.75), the area under the receiver operating characteristic curve (>0.84) and accuracy (>0.75) attributes for 2017, 2018 seasons and two seasons combined test dataset. [ABSTRACT FROM AUTHOR]

• Leaf level VIS–NIR sensing was assessed for grapevine GLRaV–3 detection. • Salient feature wavelengths were 1001, 1027 and 1052 nm. • Features were robust to detect GLRaV–3 symptoms at asymptomatic stage. • QDA performed better than Naïve Bayes in classifying infected samples. Grapevine leafroll disease (GLD) is one of the major threats to wine grapes (Vitis vinifera) causing substantial economic losses to the growers. This study was undertaken to evaluate the applicability of visible and near infrared (VIS-NIR) spectroradiometery as a rapid, robust and non–destructive optical sensing method for the detection of Grapevine leafroll - associated virus 3 (GLRaV-3) at different phenological stages in a red-berried wine grape cultivar. Using VIS-NIR spectroradiometer, data was collected from the healthy and GLRaV-3-infected leaf samples from cv. Cabernet Sauvignon for two seasons at specific intervals during asymptomatic and symptomatic stages of the disease. Fiber optic leaf clip was used to collect spectral responses from grapevine leaves under field conditions. Salient feature extraction using stepwise multilinear regression and partial least square regression methods showed significant differences between healthy and virus–infected leaves in the visible (351, 377, 501, 526, 626 and 676 nm) and near infrared (701, 726, 826, 901, 951, 976, 1001, 1027, 1052 and 1101 nm) regions. Spectral wavelengths from near infrared region (1001, 1027 and 1052 nm) were validated at different phenological stages spanning both asymptomatic and symptomatic stages of the disease. Selected spectral wavelengths demonstrated robustness in virus detection with overall classification accuracies in the range of 75–99% using quadratic discriminant analysis (QDA) classifier. QDA based classification accuracies for healthy, infected and overall classes were significantly higher compared to Naïve Bayes classifier. The accuracy for virus detection during asymptomatic stages was not significantly different from the symptomatic phase, indicating reliability of the selected features for early detection of GLRaV–3–infected grapevines. [ABSTRACT FROM AUTHOR]