scholarly journals Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4417
Author(s):  
Uchechi F. Ukaegbu ◽  
Lagouge K. Tartibu ◽  
Modestus O. Okwu ◽  
Isaac O. Olayode
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
General Purpose ◽  
Raspberry Pi ◽  
Light Weight ◽  
Speed Controller ◽  
Li Po ◽  
Global Positioning ◽  
Aerial Vehicle ◽  
Agricultural Yield

This paper describes the development of a modular unmanned aerial vehicle for the detection and eradication of weeds on farmland. Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the problematic weeds using emerging technologies. This research has addressed the aforementioned problem. A quadcopter was built, and components were assembled with light-weight materials. The system consists of the electric motor, electronic speed controller, propellers, frame, lithium polymer (li-po) battery, flight controller, a global positioning system (GPS), and receiver. A sprayer module which consists of a relay, Raspberry Pi 3, spray pump, 12 V DC source, water hose, and the tank was built. It operated in such a way that when a weed is detected based on the deep learning algorithms deployed on the Raspberry Pi, general purpose input/output (GPIO) 17 or GPIO 18 (of the Raspberry Pi) were activated to supply 3.3 V, which turned on a DC relay to spray herbicides accordingly. The sprayer module was mounted on the quadcopter and from the test-running operation conducted, broadleaf and grass weeds were accurately detected and the spraying of herbicides according to the weed type occurred in less than a second.

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

scholarly journals A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878175 ◽  
Author(s):  
Shahrukh Ashraf ◽  
Priyanka Aggarwal ◽  
Praveen Damacharla ◽  
Hong Wang ◽  
Ahmad Y Javaid ◽  
...  
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Global Positioning System ◽  
Motion Vector ◽  
Block Matching ◽  
Positioning System ◽  
Simulation Studies ◽  
Flow Measurements ◽  
Global Positioning ◽  
Aerial Vehicle

The ability of an autonomous unmanned aerial vehicle to navigate and fly precisely determines its utility and performance. The current navigation systems are highly dependent on the global positioning system and are prone to error because of global positioning system signal outages. However, advancements in onboard processing have enabled inertial navigation algorithms to perform well during short global positioning system outages. In this article, we propose an intelligent optical flow–based algorithm combined with Kalman filters to provide the navigation capability during global positioning system outages and global positioning system–denied environments. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. We propose the application of an artificial bee colony–based block matching technique for faster optical flow measurements. To effectively fuse optical flow data with inertial sensors output, we employ a modified form of extended Kalman filter. The modifications make the filter less noisy by utilizing the redundancy of sensors. We have achieved an accuracy of ~95% for all non-global positioning system navigation during our simulation studies. Our real-world experiments are in agreement with the simulation studies when effects of wind are taken into consideration.

Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat

2013 ◽  
Vol 15 (1) ◽  
pp. 44-56 ◽  
Author(s):  
D. Gómez-Candón ◽  
A. I. De Castro ◽  
F. López-Granados
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Aerial Vehicle

scholarly journals Penentuan Lintasan Pergerakan Quadcopter Berbasis GPS (Global Positioning System)

2019 ◽  
Vol 1 (2) ◽  
pp. 1-14
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hadi Supriyanto
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Global Positioning System ◽  
Positioning System ◽  
Global Positioning ◽  
Aerial Vehicle ◽  
Rotary Wing

UNMANNED AERIAL VEHICLE (UAV) merupakan sebuah kendaraan udara tanpa awak yang dapat dikendalikan. Terdapat dua tipe UAV, yakni fixed wing dan rotary wing. Quadcopter menjadi salah satu tipe UAV rotary wing yang banyak digunakan dalam berbagai kebutuhan, seperti eksplorasi dan pengambilan citra. Pada penelitian ini Quadcopter berfungsi sebagai kendaraan yang harus bergerak mengikuti lintasan, dimana lintasan yang dikuti oleh Quadcopter berasal dari GPS yang dihasilkan oleh objek yang diikuti (Modul Utama). Tipe GPS yang terpasang pada Quadcopter (GPS1) maupun pada Modul Utama (GPS2) adalah  GPS Ublox NEO. Prinsip kerja sistem adalah quadcopter mengikuti Koordinat-koordinat lintasan yang dihasilkan oleh GPS1, di mana data-data lintasan GPS1 dikirim ke Quadcopter menggunakan media Bluetooth.  Dalam pergerakannya, Quadcopter akan terus-menerus membandingkan data-data koordinat yang dihasikan posisi Quadcopter dengan data-data koordinat lintasan yang sudah diterima. Pengujian pada Receiver GPS Modul Utama (GPS1) dan Receiver GPS Quadcoter (GPS2), kedua GPS mampu mendapatkan data GPS dari satelit.  Kesalahan/perbedaan data dari GPS1 dan GPS2  pada pengujian pergerakkan Quadcopter  untuk mengikuti  Modul Utama sebagai titik tujuan sebesar 53% pada garis lintang dan 51% pada garis bujur.

Light-Weight Siamese Attention Network Object Tracking for Unmanned Aerial Vehicle

2020 ◽  
Vol 40 (19) ◽  
pp. 1915001
Author(s):  
崔洲涓 Cui Zhoujuan ◽  
安军社 An Junshe ◽  
张羽丰 Zhang Yufeng ◽  
崔天舒 Cui Tianshu
Keyword(s):  
Object Tracking ◽  
Unmanned Aerial Vehicle ◽  
Light Weight ◽  
Attention Network ◽  
Aerial Vehicle ◽  
Network Object
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