A. Jafari
Shiraz University
48 Papers
186 Citations
A. Jafari is an academic researcher from Shiraz University. The author has contributed to research in topics: Biology & Engineering. The author has an hindex of 13, co-authored 43 publications. Previous affiliations of A. Jafari include Yasouj University & University of Tehran.
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Papers
Evaluation of support vector machine and artificial neural networks in weed detection using shape features
TL;DR: To enable the vision system in the detection of the weeds based on their pattern, support vector machine and artificial neural networks were employed and several shape features were integrated to establish a pattern for each variety of the plants.
291
Some physical properties of rough rice (Oryza Sativa L.) grain
M. Ghasemi Varnamkhasti,Hossein Mobli,A. Jafari,Alireza Keyhani,M. Heidari Soltanabadi,Shahin Rafiee,Kamran Kheiralipour +6 more
TL;DR: In this article, the physical properties of rough rice cultivars were determined at a moisture content of 10% (wet basis) for Sorkheh and Sazandegi cultivars.
240
Weed segmentation using texture features extracted from wavelet sub-images
TL;DR: Wavelet texture features were examined to verify their potential in weed detection in a sugar beet crop and demonstrated that they were able to effectively discriminate weeds among the crops even when there was significant amount of occlusion and leaves overlapping.
173
Effects of moisture content and level in the crop on the engineering properties of alfalfa stems
M. Nazari Galedar,A. Jafari,Seyed Saeid Mohtasebi,Ahmad Tabatabaeefar,A. Sharifi,M.J. O’Dogherty,Shahin Rafiee,G. Richard +7 more
TL;DR: In this article, the physical and mechanical properties of alfalfa stems are presented and it is concluded that an increase in moisture content of stem leads to a decrease in the tensile strength, bending stress, Young's modulus, torsional stress, modulus of rigidity and to an increase of shear strength and shearing energy.
103
Evaluation of the air-borne ultrasound on fluidized bed drying of shelled corn: Effectiveness, grain quality, and energy consumption
TL;DR: In this paper, the authors investigated the influence of high power ultrasound on a laboratory-scale fluidized bed shelled corn dryer and developed artificial neural network (ANN) simulation models for predicting the drying variables.