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نتیجه جستجو - Thrips

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Vision-based pest detection based on SVM classification method
تشخیص افت مبتنی بر بینایی بر اساس متد طبقه بندی در SVM-2017
Automatic pest detection is a useful method for greenhouse monitoring against pest attacks. One of the more harmful pests that threaten strawberry greenhouses is thrips (Thysanoptera). Therefore, the main objective of this study is to detect of thrips on the crop canopy images using SVM classification method. A new image processing technique was utilized to detect parasites that may be found on strawberry plants. SVM method with difference kernel function was used for classification of parasites and detection of thrips. The ratio of major diameter to minor diameter as region index as well as Hue, Saturation and Intensify as color indexes were utilized to design the SVM structure. Also, mean square error (MSE), root of mean square error (RMSE), mean absolute error (MAE) and mean percent error (MPE) were used for evaluation of the classification. Results show that using SVM method with region index and intensify as color index make the best classification with mean percent error of less than 2.25%.© 2017 Elsevier B.V. All rights reserved.
Keywords:Thrips | Image processing | SVM classification | Mean percent error
مقاله انگلیسی
2 Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm
تشخیص بینایی ماشین از نقص سطح در پرتقال با استفاده از یک پنجره مقایسه کشویی الگوریتم تقسیم بندی محلی-2017
Automatic detection of defective oranges by computer vision system is not easy because of the uneven lightness distribution on the surface of oranges. It means that the methods only directly using global seg- mentation provide unsatisfactory results when orange images present faint defect characters or inhomo- geneous surface. The contrast between sound and defective regions can be used to produce more accurate segmentation results, which is more capable of detecting pixels lying around the defect boundary on orange surface based on the local segmentation method. In this paper, we study and propose a sliding comparison window local segmentation algorithm and also presents the detailed image processing pro- cedure including removal of background pixels, image binarization using local segmentation, image sub- traction, image morphological modification, removal of stem end pixels for detecting surface defect in an orange gray-level image. This method is an original contribution that allows successful segmentation of various types of surface defects (e.g., insect injury, wind scarring, thrips scarring, scale infestation, canker spot, dehiscent fruit, copper burn, phytotoxicity).The image segmentation algorithm was tested with 1191 samples of oranges. The proposed algorithm was able to correctly detect 97% of the defective orange. Future work will be focused on whole surface and fast on-line inspection.© 2017 Published by Elsevier B.V.
Keywords:Defect detection | Computer vision | Image local segmentation | Orange surface defect
مقاله انگلیسی
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