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Anti-hail (protective) netting was originally developed to protect horticultural crops from hail damage. Netting reduces the amount and modifies the light reaching the orchard canopy. It also has the potential to optimize conditions for canopy and fruit growth and mitigate abiotic stress as climate change leads to increased temperatures. This study measured the effect of different colors of netting on the above and below-ground environment and apple sunburn incidence in a 3-year-old 'Honeycrisp' apple orchard growing in an irrigated desert climate in comparison to a traditional uncovered control. Netting did not affect air temperature or relative humidity within the orchard canopy, but reduced wind speed by 40% compared to the uncovered control. Netting reduced soil temperature and improved soil moisture at 20 and 40 cm depths throughout the study period compared to the uncovered control. Amongst different colors of netting tested in this study, pearl and blue netting significantly reduced soil temperature compared to red netting. Netting also reduced photosynthetically active radiation (PAR) by approximately 20% and strongly reduced fruit surface temperature during hot periods. During full sunlight, differences in maximum fruit surface temperature between the uncovered control and the protective netting were 2.6 to 4.3°C under full sun conditions and reduced the incidence and severity of sunburn measured at harvest. As temperatures warm in the future, netting provides a viable option to mitigate some of the negative effects of excessive temperature and light on apple production in hot, dry growing regions.

Bitter pit is a physiological disorder that is defined as brown, corky and roundish lesions, which can develop in apples before and after harvest. This disorder greatly reduces the product utilization value of the fruit, and can result in several million dollar economic loss to the apple industry. Computed Tomography (CT) imaging is a non-destructive and rapid sensing technique that can be applied to packaged apples. In this study, healthy and bitter pit Honeycrisp apples were harvested from two field sites and stored for 63 days. CT images of the sampled apples were collected on 0, 7, 14, 21, 35 and 63 days after harvest. Images were analyzed to estimate the total pit area in each of the individual apples and were related to pit incidence and progression in different stages of storage. Results showed pit development during the storage period in bitter pitted apples. The rate of progression differed in samples collected from different field sites. Further analysis for pit distribution along each of the bitter pit affected apples showed 54% of pits located at the calyx-end of apples in comparison with middle and stem-end. Classification of healthy and bitter pitted apples using logistic regression based method resulted in false negative of 7-21%.

Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity – from simple RGB to multispectral imaging to hyperspectral system – were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating ( r = 0.51, P < 0.05). The features extracted from RGB images (e.g., maximum length of senesced leaves, area of senesced leaves, ratio between senesced and healthy leaf area) and multispectral images (e.g., vegetation indices) also demonstrated potential in evaluation of disease rating (| r | > 0.35, P < 0.05). The average classification accuracy achieved using visible-near infrared spectral reflectance data during the classification of susceptible from symptomless groups ranged between 71 and 93% using partial least square regression and quadratic support vector machine. In addition, fire blight disease ratings were compared with normalized difference spectral indices (NDSIs) that were generated from visible-near infrared reflectance spectra. The selected spectral bands in the range 710–2,340 nm used for computing NDSIs showed consistently higher correlation with disease severity rating than data acquired from RGB and multispectral imaging sensors across multiple seasons. In summary, these specific spectral bands can be used for evaluating fire blight disease severity in apple breeding programs and potentially as early fire blight disease detection tool to assist in production systems.

13Bitter pit is a serious disorder in apples. The current technique involves manual inspection of 14 fruits prior to packaging for fresh market. Therefore, the main objective of this study was to 15 evaluate the near infrared (NIR) spectroscopy for bitter pit detection in apples. The spectral 16 reflectance data were collected from healthy and bitter pitted honeycrisp apples from two 17 different locations. Apples were stored in cold storage and spectra were acquired at 0, 35 and 63 18 days after harvest (DAH). On each of the DAH, each of the 40 apples (20 healthy and 20 bitter 19 pitted) were analyzed to acquire three spectra per location with three marked locations per fruit. 20 Suitable spectral features were selected using stepwise multilinear regression and rank feature 21 technique. The spectral bands of 971.2, 978.0, 986.1, 987.3, 995.4, 1131.5, 1135.3, 1139.1 and 22 1142.8 nm were identified as the bands though to be associated with bitter pit in honeycrisp 23 apples. Feature datasets were evaluated using quadratic discriminant analysis and support vector 24 machine classifiers to evaluate robustness of these features in bitter pit detection. Overall, 25 classifiers performance comparison revealed that bitter pitted honeycrisp apples can be 26 distinguished with average accuracy in the range of 78-87 %. Based on spectral features of this 27 study, spectra related to cell membrane water-soaked regions that contribute to spectral variation 28 might have been identified. Our on-going studies are further validating those bands on 29 Honeycrisp and other apple cultivars and using different spectral band selection methods towards 30 developing a portable sensing module for apple bitter pit detection.31

Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible–near-infrared spectrometry-based reflectance spectral data (350–2500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 °C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550–1700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible–near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90–100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines.

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