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🌾Smart Agriculture Analytics: From Sensors to Harvest 🌿

🌾Smart Agriculture Analytics: From Sensors to Harvest 🌿

Welcome to this Power BI tutorial on the Smart Agriculture Analytics Dashboard, where we transform raw farm data into actionable insights — from sensors to harvest.

📊 What You’ll Learn in This Tutorial:

✅ How to visualize key agricultural metrics like:

  • Average humidity, NDVI index, pesticide use, and rainfall
  • Soil moisture, soil pH, and average temperature

✅ Breakdown of:

  • Crop yield by disease status and crop type
  • Fertilizer type effectiveness (Inorganic, Organic, Mixed)
  • Irrigation method impact on yield
  • Sunlight hours vs. yield

In this video, you’ll learn how to build and explore a dynamic, interactive dashboard designed for precision agriculture. Perfect for agronomists, agri-tech analysts, educators, and policy-makers, this dashboard combines environmental, crop, and regional data to optimize agricultural productivity.

In the dataset that we are using in this Tutorial, Target field is:

🌾 yield_kg_per_hectare — What It Means:

This tells you how much crop was harvested per unit of land.

  • “kg” = kilograms (how much the crop weighs)
  • “per hectare” = the size of land (1 hectare = 10,000 square meters, about the size of a sports field)

👉 So, yield_kg_per_hectare means:

“How many kilograms of crops were produced from one hectare of farmland.“

Dataset Link : https://www.kaggle.com/datasets/atharvasoundankar/smart-farming-sensor-data-for-yield-prediction

📌 Example:

If yield_kg_per_hectare = 3,500, it means:

“From every hectare of land, the farm harvested 3,500 kilograms of crops.”

This is the main target we’re trying to predict or improve using sensor data (like temperature, humidity, etc.).

Sure! Here’s a simple explanation of the Normalized Difference Vegetation Index (NDVI):

🌿 NDVI (Normalized Difference Vegetation Index) — In Simple Words:

NDVI tells us how healthy and green the plants are.

  • It’s a value between 0.3 and 0.9 in your data.
  • The closer the value is to 1.0, the healthier and greener the vegetation is.
  • If it’s closer to 0.3, the plants might be less healthy or the land may have less vegetation.

📌 Example:

  • 0.8 or 0.9 → Very healthy, green crops 🌱
  • 0.4 or 0.5 → Not-so-healthy plants or sparse vegetation 🍂

🌞 How It Works (Very Simply):

NDVI uses sunlight reflected from the plants:

  • Healthy plants reflect a lot of near-infrared light (we can’t see it).
  • They reflect less visible red light.
  • NDVI uses this difference to measure plant health from satellite or sensor data.