scholarly journals Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 162
Author(s):  
Jun Qin ◽  
Biao Wang ◽  
Yanlan Wu ◽  
Qi Lu ◽  
Haochen Zhu
Keyword(s):  
Spatial Information ◽  
Practical Method ◽  
Pine Wood Nematode ◽  
Information Retention ◽  
Accurate Identification ◽  
Pine Wood ◽  
Aerial Vehicle ◽  
Information Module ◽  
Uav Images ◽  
And Control

Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

Study on Pine Wilt Disease and its Control Situation

2011 ◽  
Vol 55-57 ◽  
pp. 567-572
Author(s):  
He Li ◽  
Guo Ying Zhou ◽  
Ju Nang Liu ◽  
Huai Yun Zhang
Keyword(s):  
Southern China ◽  
Pine Wilt Disease ◽  
Distribution Patterns ◽  
The United States ◽  
Wilt Disease ◽  
Effective Control ◽  
Pine Wood Nematode ◽  
Pine Wood ◽  
Pine Wilt ◽  
And Control

The pine is an important tree species in China, while the pine wilt disease is a devastating disease of pine trees. Pine wood nematode (Bursaphelechus xylophilus Steiner & Buhere, 1934 Nickle, 1981) is a world recognized major alien species in the world. It’s from the United States, but pine wood nematode does not seriously endanger the pine in China; the most endangered place is Japan, after several decades of research and control, it has been basically able to control the occurrence of pine wilt disease. The spread trend of pine wilt disease in China grows more and more obvious, which has become an important intrusion pathogenic organism. It not only causes a devastating threats the million hectares pine forest in southern China, but also affects China’s economy and social sustainable development, what’s more, it will damage some of famous scenic spots and cultural heritage sites in China. Meanwhile, the pine wood nematode is a technical barrier, which seriously impacts on China’s import and export trade. The pathogen, pathogenesis, modes of transmission and means of distribution patterns, rapid detection and control methods of pine wilt disease are reviewed in this paper, and we hope that it can provide references for effective control of pine wilt disease in China.

scholarly journals Lateral Control Implementation for an Unmanned Aerial Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
R. Samar ◽  
M. Zamurad Shah ◽  
M. Nzar
Keyword(s):  
Flight Control ◽  
Flight Test ◽  
Practical Method ◽  
Roll Angle ◽  
Test Results ◽  
Guidance And Control ◽  
Yaw Rate ◽  
Guidance Law ◽  
Aerial Vehicle ◽  
And Control

This paper presents practical aspects of guidance and control design for UAV and its flight test results. The paper focuses on the lateral-directional control and guidance aspects. An introduction to the mission and guidance problem is given first. Waypoints for straight and turning flight paths are defined. Computation of various flight path parameters is discussed, including formulae for real-time calculation of down-range (distance travelled along the desired track), cross-track deviation, and heading error of the vehicle; these are then used in the lateral guidance algorithm. The same section also describes how to make various mission-related decisions online during flight, such as when to start turning and when a waypoint is achieved. The lateral guidance law is then presented, followed by the design of a robust multivariable H∞ controller for roll control and stability augmentation. The controller uses the ailerons and rudder for control of roll angle and stabilization of yaw rate of the vehicle. The reference roll angle is generated by the nonlinear guidance law. The sensors available on-board the vehicle do not measure yaw rate; hence, a practical method of its estimation is proposed. The entire guidance and control scheme is implemented on the flight control computer of the actual aerial vehicle and taken to flight. Flight test results for different mission profiles are presented and discussed.

Applying coherent design to uninhabited aerial vehicle operations and control stations

2007 ◽  
Author(s):  
Luis N. Gonzalez Castro ◽  
Amy R. Pritchett ◽  
Daniel P. J. Bruneau ◽  
Eric N. Johnson
Keyword(s):  
Aerial Vehicle ◽  
And Control

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

scholarly journals Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

2021 ◽  
Vol 13 (7) ◽  
pp. 1238
Author(s):  
Jere Kaivosoja ◽  
Juho Hautsalo ◽  
Jaakko Heikkinen ◽  
Lea Hiltunen ◽  
Pentti Ruuttunen ◽  
...  
Keyword(s):  
Reference Data ◽  
Measurement Techniques ◽  
Synthetic Data ◽  
Ground Truth ◽  
Precision Farming ◽  
Aerial Imaging ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

scholarly journals Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 66
Author(s):  
Rahee Walambe ◽  
Aboli Marathe ◽  
Ketan Kotecha
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Test Time ◽  
Complex Task ◽  
Open Domain ◽  
End User ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

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