R
Reza Ehsani
Researcher at University of California, Merced
Publications - 150
Citations - 5679
Reza Ehsani is an academic researcher from University of California, Merced. The author has contributed to research in topics: Computer science & Multispectral image. The author has an hindex of 30, co-authored 138 publications receiving 4371 citations. Previous affiliations of Reza Ehsani include University of Florida & University of California.
Papers
More filters
Journal ArticleDOI
Review: A review of advanced techniques for detecting plant diseases
TL;DR: In this article, the authors present a review of the currently used technologies that can be used for developing a ground-based sensor system to assist in monitoring health and diseases in plants under field conditions.
Journal ArticleDOI
Applications of nanomaterials in agricultural production and crop protection: A review
TL;DR: Preliminary studies show the potential of nanomaterials in improving seed germination and growth, plant protection, pathogen detection, and pesticide/herbicide residue detection.
Journal ArticleDOI
Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees
Francisco Garcia-Ruiz,Sindhuja Sankaran,Joe Mari Maja,Won Suk Lee,Jesper Rasmussen,Reza Ehsani +5 more
TL;DR: High-resolution aerial sensing has good prospect for the detection of HLB-infected trees and among the tested classification algorithms, support vector machine (SVM) with kernel resulted in better performance than other methods such as SVM (linear), linear discriminant analysis and quadratic discriminantAnalysis.
Journal ArticleDOI
A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging
TL;DR: Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability.
Journal ArticleDOI
A methodology for fresh tomato maturity detection using computer vision
TL;DR: A method for detecting the maturity levels (green, orange, and red) of fresh market tomatoes by combining the feature color value with the backpropagation neural network (BPNN) classification technique is proposed.