Abstract:Comparison of feature detectors and descriptors and assessing their performance is very important in computer vision. In this study, we evaluate the performance of seven combination of well-known detectors and descriptors which are SIFT with SIFT, SURF with SURF, MSER with SIFT, BRISK with FREAK, BRISK with BRISK, ORB with ORB and FAST with BRIEF. The popular Oxford dataset is used in test stage. To compare the performance of each combination objectively, the effects of JPEG compression, zoom and rotation, blur, viewpoint and illumination variation have investigated in terms of precision and recall values. Upon inspecting the obtained results, it is observed that the combination of ORB with ORB and MSER with SIFT can be preferable almost in all possible situations when the precision and recall results are considered. Moreover, the speed of FAST with BRIEF is superior to others.

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Background The sustainable management of agricultural resources requires the integration of cutting‐edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible‐near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. Results After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. Conclusion The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry

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