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Analyze Satellite Data to Investigate Plant Health

BeginnerGuided Project

Use satellite data to analyze plant health and gain insights for sustainable agriculture and plant-health monitoring. Learn techniques for using geospatial APIs and a popular and accurate index to plan for precision agriculture, resource optimization, and sustainable food production. This hands-on project equips you to harness the latest geospatial technology to ensure a healthy plant ecosystem.

4.3 (12 Reviews)

Language

  • English

Topic

  • Data Science

Industries

  • Agriculture

Skills You Will Learn

  • API, Environmental Intelligence, Geospatial, Python, Data Visualization

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • July 13, 2025
About this Guided Project
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This project is outdated. It will be updated later.

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Healthy plant life is crucial to our own survival. In this project, you will engage in a hands-on analysis of satellite sensor data for assessing plant health using the Normalized Difference Vegetation Index (NDVI). NDVI is a widely used remote sensing technique that leverages the visible and near-infrared bands of the electromagnetic spectrum to gauge vegetation vigor and health.

You will have access to a dataset of satellite images from publicly available source, such as NASA's Earth Observing System Data and Information System (EOSDIS) through easy to use Geospatial API provided by the IBM Environmental Intelligence. Using a simple programming environment like Python with libraries such as NumPy and Matplotlib, this project guides you through the process of extracting relevant spectral bands, calculating NDVI values, and visualizing the spatial distribution of plant health across a specific agricultural area. You'll learn how technology can transform raw satellite data into actionable insights for agriculture.

The integration of this technology provides significant benefits, particularly for precision agriculture. By enabling farmers and agricultural professionals to monitor plant health efficiently over large areas, NDVI analysis helps identify potential issues such as water stress, pest infestations, or nutrient deficiencies early on. This proactive approach promotes timely interventions, resource optimization, and, potentially, improved crop yields. 

The project will highlight the scalability of satellite data analysis, showcasing how the same techniques can be applied to different regions or over time to track changes in vegetation health. You'll gain practical skills in data manipulation and visualization, empowering you to apply these methods in various fields that require environmental monitoring.

The intended audience for this project includes students and professionals in agriculture, environmental science, or data analysis, and anyone interested in leveraging remote sensing technologies. The project is designed to be completed within 30 minutes, providing a concise yet comprehensive introduction to NDVI analysis using satellite data.

Note: To complete this project, you must register to receive free access to the IBM Environmental Intelligence API keys. The process is simple and steps are provided in the project.


What you'll learn

On completing this project, you will:
  • Understand the fundamentals of geospatial APIs and their role in environmental intelligence.
  • Learn how to use Python to interact with geospatial APIs.
For example, notice this sine wave pattern, which is likely due to the seasonal variation in vegetation activity like harvest and re-growth. NDVI values tend to oscillate over the course of a year due to changes in vegetation growth driven by factors like temperature, precipitation, and sunlight. This project will show you how to generate and analyze this type of result.
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What you'll need

To successfully complete this guided project, you will need:
  • Basic knowledge of Python programming.
  • Access to the IBM Skills Network Labs environment, which comes pre-installed with necessary tools such as Docker.
  • A current version of a web browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari for the best experience.

Instructors

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

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Neha A

Developer

Senior backend developer at IBM working on sustainability software to help tackle some real world problems like climate crisis and disaster management. I’m passionate about technology and innovation and thrive on exploring new learning opportunities to enhance and broaden my skillset.

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Contributors

Shivam Abhijeet

Growth Product Manager

I am a growth product manager for IBM's new Environmental Intelligence platform. I help developers build solutions by harnessing environmental data.

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Srikanth Chowdari Tallapaneni

Software Developer

I am a software developer with 4 years of experience, specializing in full-stack development using Angular and Spring Boot. My expertise spans across building robust web applications and delivering impactful solutions, especially in the backend and frontend realms. I leverage my skills to build scalable and efficient applications, focusing on clean code, best practices, and optimal user experience. Recently, I have ventured into the Geospatial domain, utilizing tools like QGIS and GeoJSON to enhance my understanding of location-based data and its applications. This blend of software development and domain-specific knowledge allows me to tackle diverse challenges in both traditional and emerging fields. With a strong foundation in full-stack development and a continuous drive to learn and adapt, I bring practical insights and hands-on knowledge that can help both beginners and seasoned professionals. My experience in applying software skills to new domains like Geospatial ensures that I can guide others in adapting to change and leveraging their skills in different areas. My approach to teaching focuses on practical, real-world scenarios, making complex concepts accessible and applicable.

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Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Neil MacKinnon

Senior Editor and Technical Content Producer, IBM Developer

I am a writer, editor, video producer, and digital content strategist with over 20 years of experience in technical writing, content development, and professional publishing. I provide direction and produce content for multiple communications platforms, including websites, social media, podcasts, and video channels.

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Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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