Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging

Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths

A firm texture of dry onions is important for consumer acceptance. Both the texture and dry matter content decline during storage, influencing the market value of onions. The main goal of this study was to develop predictive models that in future might form the basis for automated sorting of onions for firmness and dry matter content in the industry. Hyperspectral scanning was conducted in reflectance mode for six commercial batches of onions that were monitored three times during storage. Mean spectra from the region of interest were extracted and partial least squares regression (PLSR) models were constructed. Feature wavelengths were identified using variable selection techniques resulting from interval partial least squares and recursive partial least squares analyses. The PLSR model for firmness gave a root mean square error of cross-validation (RMSECV) of 0.84 N, and a root mean square error of prediction (RMSEP) of 0.73 N, with coefficients of determination ( R²) of 0.72 and 0.83, respectively. The RMSECV and RMSEP of the PLSR model for dry matter content were 0.10% and 0.08%, respectively, with a R² of 0.58 and 0.79, respectively. The whole wavelength range and selected wavelengths showed nearly similar results for both dry matter content and firmness. The results obtained from this study clearly reveal that hyperspectral imaging of onion bulbs with selected wavelengths, coupled with chemometric modeling, can be used for the noninvasive determination of the firmness and dry matter content of stored onion bulbs.

Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging

BACKGROUND

Fungal decay is a prevalent condition that mainly occurs during transportation of products to consumers (from harvest to consumption) and adversely affects postharvest operations and sales of citrus fruit. There are a variety of methods to control pathogenic fungi, including UV-assisted removal of fruit with suspected infection before storage, which is a time-consuming task and associated with human health risks. Therefore it is essential to adopt efficient and dependable alternatives for early decay detection. In this study, detection of orange decay caused by Penicillium genus fungi was examined using spectral imaging, a novel automated inspection technique for agricultural products.

RESULTS

The reflectance parameter (including mean reflectance) and reflectance distribution parameters (including standard deviation and skewness) of surfaces were extracted from decayed and rotten regions of infected samples and healthy regions of non-infected samples. The classification accuracy of rotten, decayed and healthy regions at 4 and 5 days after fungal inoculation was 98.6 and 100% respectively using the mean and skewness of 500 nm, 800 nm, 900 nm and modified normalized difference vegetation index (MNDVI) spectra.

CONCLUSION

Comparison of results between healthy and infected samples showed that early real-time detection of Penicillium digitatum using multispectral imaging was possible within the near-infrared (NIR) range. © 2018 Society of Chemical Industry.

Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review

Objective quality assessment and efficacious safety surveillance for agricultural and food products are inseparable from innovative techniques. Hyperspectral imaging (HSI), a rapid, nondestructive, and chemical-free method, is now emerging as a powerful analytical tool for product inspection by simultaneously offering spatial information and spectral signals from one object. This paper focuses on recent advances and applications of HSI in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables. First, the basic principles and major instrumental components of HSI are presented. Commonly used methods for image processing, spectral pretreatment, and modeling are summarized. More importantly, morphological calibrations that are essential for nonflat objects as well as feature wavebands extraction for model simplification are provided. Second, in spite of the physical and visual attributes (size, shape, weight, color, and surface defects), applications from the last decade are reviewed specifically categorized into textural characteristics inspection, biochemical components detection, and safety features assessment. Finally, technical challenges and future trends of HSI are discussed.

Presymptomatic detection of powdery mildew infection in winter wheat cultivars by laser-induced fluorescence

The sensor-based monitoring of diseases under controlled conditions establishes an objective tool that allows a better understanding of the pathogen-plant interactions in different situations. The purpose of our work was to implement the presymptomatic detection of powdery mildew on wheat leaves shortly after fungus inoculation by spectral and time-resolved laser-induced fluorescence spectroscopy. In the general scope of plant phenotyping, we hypothesized that it is possible to discriminate between wheat genotypes that are either resistant or susceptible to powdery mildew. According to our results, the presymptomatic detection of powdery mildew on wheat leaves was accomplished, irrespective of genotype, as early as one day after inoculation using the fluorescence amplitude ratio F451:F522. Similarly, the ratios F451:F522, F522:F687, and F522:F736 of the half-bandwidth are also appropriate parameters. Furthermore, in the spectral range between 410 nm and 620 nm, the mean lifetime was significantly longer in inoculated leaves than it was in control leaves. Finally, the short-term (10-12 hour) increase of the fluorescence mean lifetime at 530 nm and 560 nm following the inoculation suggests that the speed of the plant reaction might be associated to its resistance to the pathogen. Based on this information, we conclude that determinations of ultraviolet, laser-induced fluorescence intensity and lifetime are suitable approaches to presymptomatically detect powdery mildew on wheat leaves one day after inoculation.