Agro-Informatics Research
Our agricultural research centers on developing innovative precision agriculture (PA) approaches and sustainable, intelligent management practices. We leverage remote sensing and a variety of sensors and data sources to enable site-specific management (SSM). SSM involves the observation, measurement, and response to both inter and intra-field variability. Our primary objective in precision agriculture is to create decision support systems (DSS) that optimize returns on agricultural inputs while conserving valuable resources through sustainable practices. To achieve this goal, we employ agro-informatics methodologies, which encompass remote sensing from diverse platforms such as satellites, airborne vehicles, UAVs, and ground-based sensors, combined with machine learning algorithms. This integrated approach facilitates the development of precision agriculture solutions and the application of spatial decision support systems for site-specific management.
Current Research Projects
Crop mapping close to real-time in Israel: Development of an artificial intelligence system based on satellite imaging for crop detection
Research team: Dr. Offer Roznation (PI) and Dr. Tarin Paz-Kagan (Co-PI)
Students:
PhD: Mr. Lior Fine
MSc: Ms. Nechama Zipora Brickner, Mr. Avi Tolnov, Mr. Adi Edry
Using remote sensing for precise nitrogen fertilization to estimate the spatial variability in yield and fruit quality in avocados
Research team: Dr.Tarin Paz-Kagan (PI) and Dr.Ran Erel (Co-PI), Dr.Shahar Baram (CI), Dr. Noam Alkan (CI)
PhD: Mr. Iaroslav Grozdov
Identification and quantification of molds in cannabis inflorescences and prevention of their development during cultivation and storage
Research team: Dr. Yaakov Shimshoni (PI). Dr. Davide Kengisbuch (Co-PI) and Dr. Tarin Paz-Kagan (CI)
Phenotypic characterization of a major QTL that controls iron absorption potential in peanuts
Research team: Dr. Ran Hovav (PI) and Dr. Tarin Paz-Kagan (Co-PI)
Lab Engineer: Dr. Tamir Caras
Application of gibberellin treatments in young and mature avocado trees to improve growth and yield under heat stress conditions
Research team: Dr. Vered Irihimovitch (PI) and Dr.Tarin Paz-Kagan (Co-PI)
MSc: Mr. Tal Shahar
HORIZON-CL6-2022-GOVERNANCE-01 Project: 101086300 — CrackSense, Upscaling (real-time) sensor data for EU-wide monitoring of production and agri-environmental conditions
Research team:
Postdoctoral: Mr. Vladislav Dubinin
MSc: Ms. Yuval Tenenboim, Mr. Paz Adziashvili, Mr. Yaniv Sevil, Mr. Yochai Shamai
Using an artificial intelligence-based model to identify the nitrogen state in a plant by spectroscopy to examine the relationships between minerals and metabolites and develop a tool for optimal fertilization in orchards
Research team: Dr. Or Sperling (PI), Prof. Uri Yermiyahu (CI), and Dr. Tarin Paz-Kagan (Co-PI)
MSc:
Incorporation of winter tree physiology in forecasting models of orchards’ bloom and yield under climate shifts
Research team:Dr. Tarin Paz-Kagan (PI) Prof. Maciej A. Zwieniecki (CI), and Dr. Or Sperling (CI)
MSc: Oren Lanterman
Tree-based multilevel spatial decision support systems to close the yield gap in almond orchards
Research team: Dr. Baram Shahar (PI), Prof. Brown Patrick (CI), Prof. Jin Yufang (CI), Dr. Train Paz-Kagan (CI)
MSc: Ofek Woldenberg, Noam Efrat
