Course

Precision Agriculture with Python and Google Earth Engine

4.6 Basic to Advance 20 Days | 40 Hours (2 Hours per Day) Very Soon
Instructor - Highly Proficient in Geospatial Data Science, Remote Sensing, and Machine Learning

Course Overview

This 40-hour course equips learners with advanced techniques in precision agriculture using Google Earth Engine (GEE) and Python. Covering a range of topics from remote sensing and machine learning to soil health analysis, drought prediction, and yield forecasting, this course prepares participants to apply geospatial technology in agriculture.

Why Enroll in This Course?

Expert-Led Training – Learn from professionals in geospatial technology and precision farming.
Hands-On Learning – Work on real-world projects using satellite and drone data.
Advanced Techniques – Apply ML/DL models, soil analysis, and crop monitoring.
Comprehensive Materials – Access study PDFs, PPTs, and code scripts for each module.

Who Should Take This Course?

Agriculture researchers, students, and professionals interested in geospatial technology. Data scientists and analysts looking to apply ML & DL to agriculture and remote sensing. Environmental specialists working on soil moisture, crop monitoring, and hydrological modeling. GIS & remote sensing professionals aiming to enhance their skills with GEE and Python.

Course Features

Live & Recorded Classes – Interactive sessions with expert guidance.
Comprehensive Study Materials – PDFs, PPTs, and Code Scripts for reference.
Real-World Applications – Work on soil pH mapping, yield forecasting, and crop classification.
Multi-Device Access – Learn on mobile, laptop, or PC at your convenience.

Topics of Course

Basic Topic :

Introduction & Course Overview
  • Introduction to Remote Sensing, GIS & Machine Learning applications
  • Overview of the course structure and objectives
    • Google Earth Engine Basics
  • Introduction to Google Earth Engine (GEE)
  • Basic functions: Importing datasets, visualization, and simple calculations
    • Python Basics & Data Visualization
  • Python fundamentals for geospatial analysis(GEE)
  • How to plot geospatial data in Python

    • Advance Topics :

    Machine Learning & Deep Learning Concepts
  • Fundamentals of ML & DL in geospatial analysis
  • Practical ML & DL work
    • Case Study:
  • AgriPredict: Improving Crop Yield Prediction using Different ML Techniques
  • Crop yield prediction in GEE
    • Hyperspectral Soil Moisture Prediction
  • ML & DL models for soil moisture estimation
    • Advanced Soil Moisture Estimation
  • Sentinel-2 Soil Moisture Estimation using the OPTRAM model
  • Landsat 8 Soil Moisture Estimation using the TOTRAM algorithm
  • Advanced soil moisture analysis in GEE
    • Drone Data Exploration & Visualization in GEE
  • Processing and analyzing drone imagery in GEE
    • Hydrology, Groundwater & Evapotranspiration in GEE
  • Hydrological modeling
  • Groundwater assessment
  • Evapotranspiration estimation
    • Thermal Image-Based Rice Disease Detection using DL
  • Deep learning-based disease detection from thermal imagery
    • Soil pH Mapping & Prediction with Python
  • Geospatial modeling of soil pH
  • Prediction techniques using ML
    • Vegetation & Crop Monitoring
  • Vegetation Moisture Monitoring using Landsat 8 & 9 with NDMI
  • Crop Type Detection using NDVI from Landsat & Sentinel-2 imagery
    • Potato Growth Prediction using ML Models
  • Ensemble vs. non-ensemble ML models for potato growth prediction
    • Soil Nutrient Variability & Crop Yield
  • Exploring the impact of soil nutrients on crop yield
  • Ensemble models (AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, RF)
    • Drought Mapping & Python-Based Indices Calculation
  • Drought assessment with remote sensing indices
    • Plant Disease & Weed Detection using DL
  • ML & DL models for plant disease and weed classification
    • Crop Mapping & Global Datasets
  • Crop mapping at different scales using world datasets
    • Predicting Soil Nutrients Using Hyperspectral Data
  • Comparative Analysis of Machine Learning Models for soil nutrient prediction from hyperspectral data
    • Soil Moisture Prediction in Wheat Fields
  • Comparative Analysis of Depth-Wise Soil Moisture Prediction using regression & ML
    • Land Use Land Cover (LULC) Mapping
  • ML & DL-based LULC classification
  • Python-based LULC modeling

    • 2199/-

    ENROLL NOW

    Course Name :-

    Precision Agriculture with Python and Google Earth Engine

    Instructor:-

    Highly Proficient

    Category:-

    Basic to Advanced

    Level:-

    Intermediate

    Duration:-

    20 Days | 40 Hours (2 Hours per Day)

    Mode:-

    Live & Recorded Sessions

    Access:-

    Mobile, Laptop, PC

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    Requirements

  • At SkillBuilt 24x7, we welcome everyone who's ready to grow—no experience needed! Here's all you need to get started:
  • No need for basic coding knowledge – we start from scratch
  • Beginner-friendly – perfect for absolute newcomers
  • Just bring your curiosity and eagerness to learn new things
  • A laptop is a must – your gateway to hands-on learning
  • No prior concepts required – we’ll teach you everything step by step

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