Development of Agricultural Drone for Crop Monitoring and Disease Detection

Development of Agricultural Drone for Crop Monitoring and Disease Detection

Drones with camera, GPS (Global Positioning System) and computational power makes gathering of data in large fields, faster and independent.The proposed methodology describes the design and implementation of a drone-based system that uses real-time information for crop productivity. The methodology...

Full description

Saved in:
Bibliographic Details
Published in:2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) pp. 1 - 6
Main Authors: Peeyush, K P, V, Akshith, D, Sai Dakshesh, M V S, Krishna Pranav, T, Malavika Menon
Format: Conference Proceeding
Language:English
Published: IEEE 12-07-2024
Subjects:
CNN
Convolutional neural networks
Crop Disease detection
Crops
Drone
GPS
Monitoring
Preprocessing
Prevention and mitigation
Real-time systems
Sustainable development
Web interface
Web services
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Drones with camera, GPS (Global Positioning System) and computational power makes gathering of data in large fields, faster and independent.The proposed methodology describes the design and implementation of a drone-based system that uses real-time information for crop productivity. The methodology implemented involves the use of a fabricated drone and image processing techniques to identify and classify crop diseases and extends to mapping the location of the infected plant on the web interface for disease prevention with the help of on-board components like GPS. The system captures RGB images of the leaves, processes them through image acquisition, preprocessing, segmentation and then classification using CNN (Convolutional Neural Network) model. The system is capable of displaying the analytic results of image processing on the web interface developed. This system was designed and proposed for early and real-time disease detection in rice crop.
ISSN:2766-2101
DOI:10.1109/CONECCT62155.2024.10677281