Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide higher quality pickle products and reduce potential economic losses. Research was conducted on using a hyperspectral imaging system for detecting defects on pickling cucumbers caused by mechanical stress. A near-infrared hyperspectral imaging system was used to capture both spatial and spectral information from cucumbers in the spectral region of 900 - 1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Cucumber samples were subjected to two forms of mechanical loading, dropping and rolling, to simulate stress caused by mechanical harvesting. Hyperspectral images were acquired from the cucumbers over time periods of 0, 1, 2, 3, and 6 days after mechanical stress. Hyperspectral image processing methods, including principal component analysis and wavelength selection, were developed to separate normal and mechanically injured cucumbers. Results showed that reflectance from normal or non-bruised cucumbers was consistently higher than that from bruised cucumbers. The spectral region between 950 and 1350 nm was found to be most effective for bruise detection. The hyperspectral imaging system detected all mechanically injured cucumbers immediately after they were bruised. The overall detection accuracy was 97% within two hours of bruising and it was lower as time progressed. Lower detection accuracies for the prolonged times after bruising were attributed to the self- healing of the bruised tissue after mechanical injury. This research demonstrated that hyperspectral imaging is useful for detecting mechanical injury on pickling cucumbers.
Multispectral imaging in reflectance and fluorescence modes combined with neural network analysis was used to
classify various types of apple disorder from three apple varieties (Honey Crisp, Red Cort, and Red Delicious). Eighteen
images from a combination of filter sets and three different imaging modes (reflectance, visible light induced
fluorescence, and UV induced fluorescence) were acquired for each apple sample as a base for pixel-level classification
into normal or disorder tissue. Two classification schemes, a 2-class and a multiple class, were developed and tested in
this study. In the 2-class scheme, pixels were categorized into normal or disorder tissue, whereas in the multiple class
scheme, pixels were categorized into normal, bitter pit, black rot, decay, soft scald, and superficial scald tissues. Results
indicate that single variety training under the 2-class scheme yielded highest accuracy with total accuracy of 95, 97, and
100 % for Honey Crisp, Red Cort, or Red Delicious respectively. In the multiple-class scheme, the classification
accuracy of Honey Crisp apple for normal, bitter pit, black rot, decay, and soft scald tissue was 94, 93, 97, 97, and 94 %
respectively. Through variable selection analysis, in the 2-class scheme, fluorescence models yielded higher total
classification accuracy compared to reflection models. For Red Cort and Red Delicious, models with only FUV yield
more than 95% classification accuracy, demonstrating a potential of fluorescence to detect superficial scald. Several
important wavelengths, including 680, 740, 905 and 940 nm, were identified from the filter combination analysis. The
results indicate the potential of this technique to accurately recognize different types of disorder on apple.
Opto-electronic methods represent a potential to identify the presence of insect activities on or within agricultural
commodities. Such measurements may detect actual insect presence or indirect secondary changes in the product
resulting from past or present insect activities. Preliminary imaging studies have demonstrated some unique spectral
characteristics of insect larvae on cherries. A detailed study on spectral characteristics of healthy and infested tart cherry
tissue with and without larvae (Plum Curculio) was conducted for reflectance, transmittance and interactance modes for
each of UV and visible/NIR light sources.
The intensity of transmitted UV signals through the tart cherry was found to be weak; however, the spectral properties
of UV light in reflectance mode has revealed some typical characteristics of larvae on healthy and infested tissue. The
larvae on tissue were found to exhibit UV induced fluorescence signals in the range of 400-700 nm. Multi spectral
imaging of the halved tart cherry has also corroborated this particular behavior of plum curculio larvae. The gray scale
subtraction between corresponding pixels in these multi-spectral images has helped to locate the larvae precisely on the
tart cherry tissue background, which otherwise was inseparable.
The spectral characteristics of visible/NIR energy in transmittance and reflectance mode are capable of estimating the
secondary effect of infestation in tart cherry tissue. The study has shown the shifting in peaks of reflected and
transmitted signals from healthy and infested tissues and coincides with the concept of browning of tissue at cell level as
a process of infestation.
Interactance study has been carried out to study the possibility of coupling opto-electronic devices with the existing
pitting process. The shifting of peaks has been observed for the normalized intensity of healthy and infested tissues. The
study has been able to establish the inherent spectral characteristic of these tissues. It was found that there existed
promising futuristic possibilities to use opto-electronic sensing to estimate the degree of secondary effect of insect
activities within the tissue.
This study describes machine vision procedures which are able to classify defective cherries from non-defective cherries. Defects can be divided into bruises, dry cracks, and wet cracks. Bandpass filters that enhance the intensity contrast between bruised and unbruised cherries are determined. An optimum combination of two wavelengths is identified at 750 nm (near-infrared range) and 500 nm (green range). An optimum single wavelength is identified at 750 nm. The image acquisition using these filters is described. Four detection methods using single view infrared images were studied. One method performed well in classifying cherries with bruises and wet cracks from non-defective cherries. One detection method using single view green images is studied. It performs well in classifying cherries with dry cracks from non-defective cherries. One detection method using infrared images and another using green images are used in combination to perform the detection on the entire surface of cherries. Two images, infrared and green, are taken from each of 6 orthogonal directions from the cherries. The integrated classifier misclassified 13% of non-defective cherries, 16% of bruised cherries, 0% of cherries with wet cracks, and 10% of cherries with dry cracks.