|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|84410||158880||2014||9 صفحه PDF||سفارش دهید||دانلود رایگان|
• Automatic threshold segmentation was used to extract the region of interest.
• Entropy, energy and mean features were extracted from the region of interest.
• Fuzzy-rough set based on the thermal charge algorithm was used to select wavelengths.
• The developed models yielded good accuracy for detecting vegetable soybeans.
Insects in vegetable soybean undermine the quality and safety of soybean products. Thus, a non-destructive technique of detecting insect-damaged vegetable soybean must be developed. An efficient detection method based on a hyperspectral image was proposed by selecting the region of interest (ROI) through automatic threshold segmentation and optimal wavelength selection using the fuzzy-rough set model. For the 362 samples of beans, three image features (i.e., entropy, energy, and mean) of the ROI were extracted as classification features, whose spectral region covered 400–1000 nm and contained 94 wavelengths. Three or less optimal wavelengths were then selected using a fuzzy-rough set model based on the thermal charge algorithm (FRSTCA). Support vector data description (SVDD) was used to develop classification models for the insect-damaged soybean. For the prediction samples of the beans, the classification results indicated that the normal samples were 100.0% correctly classified using the automatic extracting ROI method based on automatic threshold segmentation. The classification accuracy for the insect-damaged samples was 91.7%, and a 98.8% overall classification accuracy was achieved with the FRSTCA selecting two wavelengths.
Journal: Computers and Electronics in Agriculture - Volume 106, August 2014, Pages 102–110