Reza Ehsani

Professor

University of California

United States

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Dr. Reza Ehsani is a Professor at the University of California, USA. His research interests include mobile agricultural systems, precision agriculture, and machine learning applications in agriculture. Recent articles authored by Dr. Ehsani explore topics such as vehicle platooning, orchard monitoring, and the integration of renewable energy in agricultural practices.

Articles (22)

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2020

Collaborators (7)

Stavros Vougioukas

Asst. Professor

University of California, Davis

UNITED STATES

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Ricardo de Castro

University of California Merced

UNITED STATES

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Luca Brillante

Bronco Wine Company Chair in Viticulture

California State University, Fresno

UNITED STATES

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Ryan D. Baxter

University of California

UNITED STATES

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Arash Toudeshki

UNITED STATES

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Vijay Kumar

Professor

University of Pennsylvania

UNITED STATES

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Jeanette Cobian-Iñiguez

Assistant Professor

University of California Merced

UNITED STATES

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Reza Ehsani

Research Interests

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About

Articles (22)

Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset

Unlocking the potential of electric and hybrid tractors via sensitivity and techno-economic analysis

Power Prediction in Ground Mobile Agricultural Robots: A Context-Aware Deep Learning Framework

Online Centralized MPC for Lane Merging in Vehicle Platoons

Techno-economic analysis of mobile agrivoltaic systems leveraging excess power to hydrogen (case study: Strawberry field in California USA)

Assessing Crucial Shaking Parameters in the Mechanical Harvesting of Nut Trees: A Review

A practical data-driven approach for precise stem water potential monitoring in pistachio and almond orchards using supervised machine learning algorithms

Effectiveness of post-harvest treatments for Manzanilla table olives and the suitability of Hojiblanca as a substitute for mechanical harvesting

Robustness improvement of optimal control in terms of RBFNN with empirical model reduction and transfer learning

Power Prediction in Ground Mobile Agricultural Robots: A Context-Aware Deep Learning Framework

Predictive Neural Network Modeling for Almond Harvest Dust Control

Advancing grape chemical analysis through machine learning and multi-sensor spectroscopy

Simultaneous Trunk and Canopy Shaking Improves Table Olive Harvester Efficiency versus Trunk Shaking Alone

Identification of volatile organic compounds (VOCs) by SPME-GC-MS to detect Aspergillus flavus infection in pistachios

Neural Networks Based Optimal Tracking Control of a Delta Robot With Unknown Dynamics

Three-dimensional quantification of apple phenotypic traits based on deep learning instance segmentation

Design of an Integrated Controller for a Sweeping Mechanism of a Low-Dust Almond Pickup Machine

An Experimental Study and Mathematical Modeling of Vibration Transfer in Pistachio Trees Using an Inertia-Type Trunk Shaker and Field-Adapted Wireless Sensors

SARS-CoV-2 pseudotyped virus persists on the surface of multiple produce but can be inactivated with gaseous ozone

Estimation of proper shaking parameters for pistachio trees based on their trunk size

Yield estimation of citrus fruit using rapid image processing in natural background

Application of ozone for degradation of mycotoxins in food: A review

Collaborators (7)

Stavros Vougioukas

Ricardo de Castro

Luca Brillante

Ryan D. Baxter

Arash Toudeshki

Vijay Kumar

Jeanette Cobian-Iñiguez

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