Deep Learning for Smart Plant Weed Applications Employing an Unmanned Aerial Vehicle

Ukaegbu, Uchechi; Mathipa, Ledile; Malapane, Maite; Tartibu, Lagouge K.; Olayode, Isaac Oyeyemi

doi:10.1109/icmimt55556.2022.9845337

2022 IEEE 13th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT) 2022

DOI: 10.1109/icmimt55556.2022.9845337

|View full text |Cite

Deep Learning for Smart Plant Weed Applications Employing an Unmanned Aerial Vehicle

Uchechi Ukaegbu

Ledile Mathipa

Maite Malapane

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Flight performance indicators using anThe results reported in Table1were considered to improve the drone stability, efficiency, and performance of flight maneuverability(Ukaegbu, Mathipa, Malapane, Tartibu, & Olayode, 2022).…”

mentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle incorporating a Convolutional Neural Network for Weed Detection

Mathipa¹,

Ukaegbu²,

Tartibu³

2022

ICONIC

View full text Add to dashboard Cite

This paper introduces the development of an Unmanned Aerial Vehicle (UAV) as an efficient asset that incorporates a high-performing pre trained architecture for weed classification and detection in real-time. The UAV includes a modular sprayer and embedded systems. Weeds provide a continuous threat to the cultivation of agricultural plants and reduce yield, as well as increase waste. To address the issue, an autonomous system and intelligent sprayer are described to spot weeds or undesirable plants and spray herbicides on desired places. The effectiveness of five various pre-trained architectures (AlexNet, GoogleNet, VGG19, DenseNet201, and ResNet50) for image classification and prediction are investigated, and the top performing model was determined. The best-performing trained model, ResNet 50, achieved a training accuracy of 98.57 percent and a validation accuracy of 100 percent at 25 epochs.

show abstract

mentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle incorporating a Convolutional Neural Network for Weed Detection

Mathipa¹,

Ukaegbu²,

Tartibu³

2022

ICONIC

View full text Add to dashboard Cite

show abstract

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation

Singh,

Pratap,

Mehta

et al. 2024

IoT

View full text Add to dashboard Cite

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Deep Learning for Smart Plant Weed Applications Employing an Unmanned Aerial Vehicle

Cited by 2 publications

References 7 publications

Development of an Unmanned Aerial Vehicle incorporating a Convolutional Neural Network for Weed Detection

Development of an Unmanned Aerial Vehicle incorporating a Convolutional Neural Network for Weed Detection

Smart Agriculture Drone for Crop Spraying Using Image-Processing and Machine Learning Techniques: Experimental Validation

Contact Info

Product

Resources

About