DALL·E 2023-12-01 16.04.27 - An image illustrating 'Cotton Crop Health Monitoring through Drone Imagery and IoT Sensors'. The scene should depict a drone flying over a vast cotton

Cotton Crop Health Monitoring through Drone Imagery and IoT Sensors

Remote Sensing and Precision Agriculture

Cotton Crop Health Monitoring through Drone Imagery and IoT Sensors

Detection & segmentation of anatomical structures in retinal images
Healthy, sustainable and inclusive food system is critical for developing effective food chain of the world. In spite of having tremendous potential in agriculture, the world is facing food shortage due to various reasons. One of the reasons is lower yield of crops due to pest attacks. The aim of this project is to develop a prototype that can timely detect health of crop and give information about pest infested areas of crop. After collecting multispectral data of different crops using the Sentera Single NDVI sensor from a drone, it is processed it to detect pest-infested regions. The web application highlighting pest-infested regions makes it easier for farmers and agricultural experts to monitor their crops' health and take necessary actions to prevent pest infestations, thereby improving crop yield and reducing financial losses.

Detection & segmentation of anatomical structures in retinal images

Faculty

Students

  • Muhammad Usama (BEE-11)

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