WILD HOPPER Prototype for Forest Firefighting

In Europe, fire represents an important issue for a lot of researchers due to economic losses, environmental disasters, and human death. In the last decade, the European parliament sheds light upon this problem by dealing with the community project” Forest Focus”. Thus, researchers and scientific research departments of European companies begin to work on solving and creating different techniques to deal with such a problem, these research centers found that the most attractive and accurate way of solving such a problem was using an Unmanned Aerial Vehicle (UAV). In this paper, the research center at Drone Hopper Company analysis the deficiencies for forest fire fighting systems, in order to start designing its new prototype of a special drone named WILD HOPPER, solving all the shortcomings of similar systems. This paper is the first of a group of research papers that will take place during designing and producing our WILD-HOPPER system.

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A Forest Fire Identification Method for Unmanned Aerial Vehicle Monitoring Video Images*

2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC) ◽

10.1109/gncc42960.2018.9019073 ◽

2018 ◽

Author(s):

Wenzhao Feng ◽

Chunhe Hu ◽

Junguo Zhang ◽

Hao Yan

Keyword(s):

Unmanned Aerial Vehicle ◽

Forest Fire ◽

Vehicle Monitoring ◽

Identification Method ◽

Video Images ◽

Aerial Vehicle

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Unmanned aerial vehicle based forest fire monitoring and detection using image processing technique

2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC) ◽

10.1109/cgncc.2016.7829074 ◽

2016 ◽

Cited By ~ 7

Author(s):

Chi Yuan ◽

Khaled A. Ghamry ◽

Zhixiang Liu ◽

Youmin Zhang

Keyword(s):

Image Processing ◽

Unmanned Aerial Vehicle ◽

Forest Fire ◽

Processing Technique ◽

Image Processing Technique ◽

Fire Monitoring ◽

Aerial Vehicle

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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Sensors ◽

10.3390/s21196540 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6540

Author(s):

Qian Pan ◽

Maofang Gao ◽

Pingbo Wu ◽

Jingwen Yan ◽

Shilei Li

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicle ◽

Recognition Accuracy ◽

Yellow Rust ◽

Semantic Segmentation ◽

High Accuracy ◽

Economic Losses ◽

Support Vector ◽

Aerial Vehicle ◽

Uav Images

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

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The Extraction of Wheat Lodging Area in UAV’s Image Used Spectral and Texture Features

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.651-653.2390 ◽

2014 ◽

Vol 651-653 ◽

pp. 2390-2393 ◽

Cited By ~ 6

Author(s):

Hai Ying Liu ◽

Gui Jun Yang ◽

Hong Chun Zhu

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Scientific Research ◽

Object Oriented ◽

Texture Features ◽

Important Application ◽

Extraction Technology ◽

Research Results ◽

Aerial Vehicle ◽

First Time

Wheat lodging makes great effects on the output and subsequent production, so we need to know the situation of wheat lodging at the first time quickly and efficiently. The characters of Unmanned Aerial Vehicle remote sensing just meet the demands. Firstly, the spectral and texture features are analyzed, and the area of wheat lodging is extracted using methods of object-oriented and the extraction is analyzed and compared. The research results of this paper is the successful practice of Agricultural scientific research and application by using Unmanned Aerial Vehicle remote sensing and Object-oriented extraction technology, so it have important application value and meaningful.

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