Aflatoxins are fungal toxins produced by Aspergillus flavus. Food and feed crops get contaminated with carcinogenic aflatoxins, which often results in economic losses as well as serious health issues. Grain elevators need to unload, on average, one 50,000-pound truckload every two minutes. Current chemical and optical methods for aflatoxin detection cannot meet the screening requirements. Therefore, a high speed batch screening system with reliable accuracy is necessary. The contaminated corn kernels were prepared in our laboratory by artificial inoculation of corn ears. One hundred 200g samples were selected for analysis. To develop a high speed multispectral screening system, two high performance cameras in conjunction with dual UV excitation sources and novel image processing software were utilized to collect fluorescence images of each sample. Each camera simultaneously captures a single band fluorescence image (436 nm and 532 nm) from corn samples, and the detection software processes the images to automatically detect contaminated kernels by using a normalized difference fluorescence index. Each sample was imaged/screened four times, and screened samples were chemically analyzed for aflatoxin content. All samples were shuffled between imaging repetitions to increase the likelihood of screening both sides of every kernel. Processing time for each screening was about 0.7s, and an optimal result of 98.65% was achieved for sensitivity and 96.6% for specificity.
Levee slides may result in catastrophic damage to the region of failure. Remote sensing data, such as synthetic aperture radar (SAR) images, can be useful in levee monitoring. Because of the long length of a levee, the image size may become too large to use computationally expensive methods for quick levee monitoring, so time-efficient approaches are preferred. The popular support vector machine classifier does not work well on the original three polarized SAR magnitude bands without spatial feature extraction. Gray-level co-occurrence matrix is one of the most common methods for extracting textural information from gray-scale images, but it may not be practically useful for a big data in terms of calculation time. In this study, very efficient feature extraction methods with spatial low-pass filtering are proposed, including a weighted average filter and a majority filter in conjunction with a nonlinear band normalization process. Experimental results demonstrated that these filters can provide comparable results with much lower computational cost.
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