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Crop Health Monitoring and Weed Detection Using Drone Technology

Harsh Shah, Simran Surve, Amar Honrao, J. W. Bakal

Abstract


In an agriculture-based economy like ours, farmers and their cultivation play a significant role. With the extension of agriculture to wider fields, manual interference to monitor and detect crop health is becoming more difficult. Unmanned aerial vehicles (UAVs) have become well-known and affordable technology for a variety of precision farm uses in recent years. Combining the capabilities of drone technology and machine learning/deep learning algorithms, we can monitor crop health and also detect unwanted weeds in the farm to take correct measures. This project aims at building a drone which is capable of assisting farmers to help them to maximize their cultivation by saving time in manual methods used for identifying weed and monitoring crop health. A drone, by combining machine learning/deep learning techniques with hardware is built to help farmers by early analysis of the applications mentioned. Using deep learning algorithms, the existing system for weed detection is improved.


Keywords


Crop health monitoring, weed detection, drone technology, Convolutional Neural Network (CNN), random forest

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References


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