Utilising Drones in Agriculture: A Review on Remote Sensors, Image Processing and Their Application
Crop production heavily relies on precise management of crops and soil properties. Factors like temperature, structure and resistivity of crops and soil play crucial roles in determining agricultural output. Drone technology has emerged as the most effective solution, facilitating tasks such as chemical spraying, fertiliser application, irrigation management, weed detection, and soil analysis. This technology enables early detection of crop and soil conditions to implement timely remedies that enhance productivity. Drones reduce laborious tasks, lower input costs, and save operational time. Drones are categorised into some groups such as fixed wing, multi rotor, hybrid, remote piloted, autonomous, surveillance, spraying and payload specific drones. Equipped with sensors such as red‐blue‐green (RGB), multispectral, hyperspectral, thermal, spectrophotometer, radiometer and light detection and ranging (LiDAR), drones capture real‐time data of crops and farmland. These images undergo processing through advanced data analytics such as deep learning and machine learning. Vegetation indices such as normalised difference vegetation index, excess greenness index, normalised difference index and ratio of vegetation index etc. are employed to differentiate objects and distinguish crops from soil backgrounds. This review discusses various drone types, their components including sensors, methods for data processing, drone applications, their benefits, existing challenges, and considerations for future advancements.
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- Published
- Jul 04, 2025
- Vol/Issue
- 3(2)
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