USP Electronic Research Repository

Smart agriculture drone for crop spraying using image - processing and machine learning techniques: experimental validation

Singh, Edward and Pratap, Aashutosh and Mehta, Utkal V. and Azid, Sheikh I. (2024) Smart agriculture drone for crop spraying using image - processing and machine learning techniques: experimental validation. IoT, 5 (2). pp. 250-270. ISSN 2624-831X

[thumbnail of IoT Agri_MDPI2024.pdf] Text - Published Version
Restricted to Repository staff only

Download (2MB)

Abstract

Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.

Item Type: Journal Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TK Electrical engineering. Electronics Nuclear engineering > Robotics and Automation
Divisions: School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User: Utkal Mehta
Date Deposited: 30 Jan 2025 00:26
Last Modified: 30 Jan 2025 00:26
URI: https://repository.usp.ac.fj/id/eprint/14728

Actions (login required)

View Item View Item