Dark Period Following UV-C Treatment Enhances Killing of Botrytis cinerea Conidia and Controls Gray Mold of Strawberries.

Strawberries are available throughout the year either from production in the field or from high and low tunnel culture. Diversity of production conditions results in new challenges in controlling diseases before and after harvest. Fungicides have traditionally been used to control these diseases; however, their limitations necessitate a search for new approaches. We found that UV-C irradiation of Botrytis cinerea, a major pathogen of strawberry, can effectively kill this fungus if a dark period follows the treatment. The inclusion of a 4-h dark period resulted in almost complete kill of B. cinerea conidia on agar media at a dose of 12.36 J/m2. The UV-C dose did not cause a reduction in photosynthesis in strawberry leaves or discoloration of sepals, even after exposing plants repeatedly (twice a week) for 7 weeks. Although irradiation of dry conidia of B. cinerea with this dose resulted in some survival, the conidia were not infective and not able to cause decay even when inoculated onto a highly susceptible mature apple fruit. Irradiation of strawberry pollen at 12.36 J/m2 did not affect pollen germination, tube growth and length in vitro, or germination and tube growth in the style of hand-pollinated emasculated strawberry flowers. No negative effect of the UV-C treatment was observed on fruit yield and quality in high tunnel culture. In the fruit and flower petal inoculation tests, the UV-C treatment was highly effective in reducing fruit decay and petal infection. This UV-C treatment with an exposure time of 60 s may be useful in controlling gray mold in tunnel production of strawberries and may also have the potential for use in intensive field and indoor production of other fruits and vegetables providing that a 4-h dark period follows the irradiation.

Microbial Load of Fresh Blueberries Harvested by Different Methods.

Currently, more and more growers are transitioning to the use of over-the-row machine harvesters for harvesting fresh market blueberries. This study assessed the microbial load of fresh blueberries harvested by different methods. Samples (n = 336) of 'Draper' and 'Liberty' northern highbush blueberries, which were harvested using a conventional over-the-row machine harvester, a modified machine harvester prototype, ungloved but sanitized hands, and hands wearing sterile gloves were collected from a blueberry farm near Lynden, WA, in the Pacific Northwest at 9 am, 12 noon, and 3 pm on four different harvest days during the 2019 harvest season. Eight replicates of each sample were collected at each sampling point and evaluated for the populations of total aerobes (TA), total yeasts and molds (YM), and total coliforms (TC), as well as for the incidence of fecal coliforms and enterococci. The harvest method was a significant factor (p < 0.05) influencing the TA and TC counts, the harvest time was a significant factor influencing the YM counts, while the blueberry cultivar was an insignificant (p > 0.05) factor for all three indicator microorganisms. These results suggest that effective harvester cleaning methods should be developed to prevent fresh blueberry contamination by microorganisms. This research will likely benefit blueberry and other fresh fruit producers.

Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield.

Fruit traits such as cluster compactness, fruit maturity, and berry number per clusters are important to blueberry breeders and producers for making informed decisions about genotype selection related to yield traits and harvestability as well as for plant management. The goal of this study was to develop a data processing pipeline to count berries, to measure maturity, and to evaluate compactness (cluster tightness) automatically using a deep learning image segmentation method for four southern highbush blueberry cultivars ('Emerald', 'Farthing', 'Meadowlark', and 'Star'). An iterative annotation strategy was developed to label images that reduced the annotation time. A Mask R-CNN model was trained and tested to detect and segment individual blueberries with respect to maturity. The mean average precision for the validation and test dataset was 78.3% and 71.6% under 0.5 intersection over union (IOU) threshold, and the corresponding mask accuracy was 90.6% and 90.4%, respectively. Linear regression of the detected berry number and the ground truth showed an R2 value of 0.886 with a root mean square error (RMSE) of 1.484. Analysis of the traits collected from the four cultivars indicated that 'Star' had the fewest berries per clusters, 'Farthing' had the least mature fruit in mid-April, 'Farthing' had the most compact clusters, and 'Meadowlark' had the loosest clusters. The deep learning image segmentation technique developed in this study is efficient for detecting and segmenting blueberry fruit, for extracting traits of interests related to machine harvestability, and for monitoring blueberry fruit development.

3D point cloud data to quantitatively characterize size and shape of shrub crops.

Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R 2 = 0.88-0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R 2 = 0.78-0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.

UV-C irradiation as a management tool for Tetranychus urticae on strawberries.

BACKGROUND: Tetranychus urticae Koch, the two-spotted spider mite, is a highly polyphagous and worldwide pest of many agricultural crops, including fruit, vegetables, and ornamentals. Typical methods of control include applications of acaricides and biological control agents. Here, we present a non-chemical technology for management of T. urticae on strawberry plants through the use of a nightly short-duration ultraviolet-C (UV-C) irradiation treatment. RESULTS: Potted strawberry plants infested with T. urticae that received a nightly 60-s exposure of UV-C irradiation had significantly fewer live mites per mid-canopy leaflet (fewer than five) than untreated control plants (> 175). Furthermore, none of the UV-C irradiated strawberry plants had any spider mite webbing; whereas, 65% of untreated plants were webbed. Tetranychus urticae feeding on untreated plants caused significant yellowing of the leaves compared with UV-C-treated plants. CONCLUSION: The UV-C irradiation treatment maintained mite populations below the accepted economic threshold of five mites per mid-canopy leaflet. No phytotoxic effects were visible on plants exposed to the short-duration nightly UV-C irradiation treatments. Further discussion is provided on the potential benefits of UV-C irradiation for mite management. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging.

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 - 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising.