The document discusses several applications of machine learning in agriculture, including using image analysis of satellite and drone pictures to monitor soil and crop health, implementing precision drip irrigation based on soil moisture and other sensor data, applying targeted herbicide through automated weed detection, enhancing crop yields by optimizing water, fertilizer and pesticide applications based on historical analytics, and precisely predicting harvest yields from images and past data to gain advantages in commodity markets.

1. Applications of ML in
Agriculture
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2. Improved Soil and Crop Health
Use image analysis on satellite and drone pictures to
detect soil moisture and crop health
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3. Efficient Water Management
Precision drip irrigation based on sensor-detected soil
moisture, rain and topography and crop type
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4. Accurate Weed Detection
Selective application of herbicide based on auto-
detection of weed reducing chemical contamination and
wastage
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5. © 2021 47Billion Inc.
Enhanced Crop Yield
Auto-formulation of recipes providing the right
combination and frequency of water, fertilizer, pesticides
applications based on the historical data analytics of
inputs and yield

6. Precise Yield Prediction
Predict the precise yield based on drone, satellite
pictures combined with historical data and gain
advantage while selling in the commodity market
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7. Anything that intrigued you?
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