UNMANNED AERIAL VEHICLES FOR SMART AGRICULTURE USING MACHINE LEARNING

Author

Dr. P.K.Choudary, B.Syamsundar, K.Upendra Raju, K.Bhargavi, K.Akshaya Kumar, J.Yasodha

Abstract

 Rising food demand and climate variability are accelerating the adoption of smart agriculture (SA), where unmanned aerial vehicles (UAV), commonly known as drones, combined with Machine Learning (ML), are transforming modern agriculture by enabling precision farming. UAVs collect high-resolution images and environmental data, while ML algorithms analyse this data to detect crop diseases, estimate soil moisture, monitor plant health, predict crop yield, and optimize irrigation. This technology helps farmers improve productivity, reduce costs, and use resources efficiently. However, challenges such as limited battery life, high costs, large data processing, and environmental conditions still affect widespread adoption. Conventional farming methods that apply water, fertilizers, and pesticides uniformly across a field are ecologically and economically unsustainable. This report details a comprehensive cyber-physical framework combining Unmanned Aerial Vehicles (UAVs) and classical Machine Learning (ML) algorithms to enable precision agriculture. UAVs bridge the operational gap between coarse satellite imagery and labor-intensive ground scouting by capturing ultra-high spatial resolution, multi-sensor data beneath cloud layers. Advanced machine learning models (such as Random Forest, XGBoost, SVR, and PLSR) act as the analytical engines to transform unstructured spatial-spectral aerial data into actionable agronomic intelligence. This integrated framework supports site-specific management fields including soil moisture tracking, crop water stress estimation, nutrient mapping, selective weed classification, disease monitoring, and highly accurate in-season yield forecasting.

Keywords

Unmanned Aerial Vehicles (UAVs), Smart agriculture, Machine learning, Sensors, IOT & AI based analysis.

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References

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