Agricultural Anomaly Detection using Temporal Dynamics of Remote Sensing Data

This project is about using artificial intelligence to interpret agricultural remote sensing data. We will develop new means to integrate repeated imagery data of targeted agricultural fields to pinpoint agronomically significant anomalies (e.g., water or nutrient stress, crop pathology, weeds, etc.) and provide field managers easy to follow recommendations guiding development of the most cost effective plans to treat these anomalies.

Faculty Supervisor:

Viacheslav Adamchuk

Student:

Md Saifuzzaman

Partner:

Horoma AI Inc.

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

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