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.

Abstract

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