دکتر سید احمد میره ای samireei@iut.ac.ir نشانی دفتر اصفهان، دانشگاه صنعتی اصفهان، دانشکده کشاورزی، گروه مهندسی بیوسیستم، صندوق پستی 84156–83111 شماره تماس +98 31 3391 3408 فکس +98 31 3391 3471 پست ها و موقعیت های شغلی و سازمانی دانشیار زمینه های مورد علاقه روشهای غیرمخرب در ارزیابی کیفی محصولات کشاورزی تکنولوژی سنسور تکنولوژی پس از برداشت Dr. Seyed Ahmad Mireei نوع: Journal عنوان عنوان کنفرانس Date Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models Scientific Reports 1405-02 The effect of UAV sprayer operational characteristics on spray deposition within the target area Smart Agricultural Technology 1404-09 Chick embryo development assessment and fertility detection using pixel-wise hyperspectral image analysis and deep learning POULTRY SCIENCE 1404-08 Use of Vis-NIR reflectance spectroscopy for estimating soil phosphorus sorption parameters at the watershed scale SOIL and TILLAGE RESEARCH 1404-02 A free-space dielectric system with X-band coaxial-to-waveguide adapters for nondestructive fertility detection in unincubated chicken eggs: Optimizing spectrum, orientation, features, and classifiers COMPUTERS AND ELECTRONICS IN AGRICULTURE 1404-01 Spatial analysis of hyperspectral images for detecting adulteration levels in bon-sorkh (Allium jesdianum L.) seeds: Application of voting classifiers Smart Agricultural Technology 1403-10 Novel feature extraction in laser light backscattering imaging for real-time monitoring of quince moisture content during hot-air drying JOURNAL OF FOOD ENGINEERING 1403-10 Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: a comparison of machine learning tools and feature selection approaches Plant Stress 1403-09 Microwave spectroscopy in a free-space arrangement for nondestructive quality assessment of chicken eggs: Comparing different measurement modes and feature selection approaches FOOD CHEMISTRY 1403-08 Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils JOURNAL OF NEAR INFRARED SPECTROSCOPY 1403-07
