Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)

By Haitao Xiang, L. Tian. 2011

473 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/680e1398a99ca9606575ec26af6fbdce7c415a6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Yanbo Huang
2. Haikuan Feng
3. Ozgur Koray Sahingoz
4. Guijun Yang
5. Zhenhai Li
6. Guisong Xia

In Environmental Engineering

1. Simon Salon
2. Alper Yilmaz
3. Haikuan Feng
4. Frantisek Chudý
5. Julián Tomastík
6. Ozgur Koray Sahingoz
7. Martin Mokros
8. Guijun Yang

In Plant Science

1. Haikuan Feng
2. Ozgur Koray Sahingoz
3. Guijun Yang
4. Zhenhai Li

Citations in the corpus

• Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “The oil engines have the advantages of strong wind resistance and long working time, while there’re disadvantages of being bulky, producing big vibration and having poor reliability, which lead to the image blur (Xiang and Tian, 2011; Sankaran et al., 2015b).”
• Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead (2021, unknown): “In this case they performed multi-temporal mapping of a fraction of vegetation at the beginning of the season through various indexes, such as CIVE, ExG, ExGR, Woebbecke Index, NGRDI and VEG [58,59].”
• Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges (2013, background): “In the last two decades, as a result of the rapid technological advancement in computation, sensor, communication and networking technologies, Unmanned Aerial Vehicles (UAVs) promise new application areas for military and civilian areas such as, relay for ad-hoc networks [1–3], search and rescue ope”
• Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides (2020, unknown): “Xiang and Tian (2011) 6.”
• UAVid: A semantic segmentation dataset for UAV imagery (2018, methodology): “s captured by UAV. Second row shows the corresponding ground truth labels. Third row shows the prediction results of MS-Dilation net+PRT+FSO model as in Tab. I. surveillance [16], [17] and monitoring [18] in the target area. They have already been used for smart farming [19], precision agriculture [”
• Early season weed mapping in rice crops using multi-spectral UAV data (2018, background): “…resolution, with pixel size of few cm, ii) less limitations imposed by weather conditions, since they can fly also on cloudy days, iii) greater flexibility in acquisition scheduling and payload options, and iv) reduced costs of vehicles and sensors (Torres-Sánchez et al. 2014; Xiang and Tian 2011).”
• Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy (2017, background): “, agriculture [22,23], damage detection of buildings [24,25], landslide research [26], research of”