scholarly journals Assessment of Image-Texture Improvement Applied to Unmanned Aerial Vehicle Imagery for the Identification of Biotic Stress in Espeletia. Case Study: Moorlands of Chingaza (Colombia)

2019 ◽  
Vol 30 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Laura Daniela Martin ◽  
Javier Medina ◽  
Erika Upegui
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Biotic Stress ◽  
Kappa Coefficient ◽  
Image Texture ◽  
Support Vector ◽  
Bhattacharyya Distance ◽  
Image Texture Analysis ◽  
Vector Machines ◽  
Aerial Vehicle ◽  
Occurrence Matrix

Espeletia is one of the most representative endemic species of moorland ecosystems, and is currently being affected by biotic stress. Meanwhile, the analysis of images obtained by means of unmanned aerial vehicle imagery has proved its usefulness in environmental monitoring activities. The present work is aimed at establishing whether image-texture analysis applied to unmanned aerial vehicle imagery from Moorlands of Chingaza (Colombia) allows the identification of biotic stress in Espeletia. To this end, this study makes use of occurrence analysis, gray-level co-occurrence matrix, and Fourier transform. Identification of healthy/unhealthy Espeletia is conducted using maximum likelihood tests and support vector machines. The results are assessed based on overall accuracy, the kappa coefficient and bhattacharyya distance. By combining spectral and image-texture information, it is shown that classification accuracy increases, reaching kappa coefficient values of 0,9824 and overall accuracy values of 99,51%. 

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 Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Machine vision for field-level wood identification

IAWA Journal ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 681-698 ◽  
Author(s):  
Bruno Geike de Andrade ◽  
Vanessa Maria Basso ◽  
João Vicente de Figueiredo Latorraca
Keyword(s):  
Intelligent Systems ◽  
Wood Anatomy ◽  
Support Vector ◽  
Technical Knowledge ◽  
Macroscopic Description ◽  
Automated Identification ◽  
Vector Machines ◽  
Texture Characteristics ◽  
Identification Of Species ◽  
Occurrence Matrix

Abstract Identifying wood species using wood anatomy is an important tool for various purposes. The traditionally used method is based on the macroscopic description of the physical and anatomical characteristics of the wood. This requires that the identifier has thorough technical knowledge about wood anatomy. A possible alternative to this task is to use intelligent systems capable of identifying species through an analysis of digital images. In this work, 21 species were used to generate a set of 2000 macroscopic images. These were produced with a smartphone under field conditions, from samples manually polished with knives. Texture characteristics obtained through a gray level co-occurrence matrix were used in developing classifiers based on support vector machines. The best model achieved a 97.7% accuracy. Our study concluded that the automated identification of species can be performed in the field in a practical, simple and precise way.

scholarly journals Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn

2020 ◽  
Vol 12 (13) ◽  
pp. 2071
Author(s):  
Hwang Lee ◽  
Jinfei Wang ◽  
Brigitte Leblon
Keyword(s):  
Linear Regression ◽  
Unmanned Aerial Vehicle ◽  
Random Forests ◽  
Vegetation Indices ◽  
N Fertilization ◽  
Yield Potential ◽  
Support Vector ◽  
Aerial Vehicle ◽  
Marginal Improvement ◽  
Canopy Nitrogen

The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by the farmer. The drawbacks of N deficiency on crops include poor plant growth, ultimately reducing the final yield potential. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery to predict canopy nitrogen weight (g/m2) of corn fields in south-west Ontario, Canada. Simple/multiple linear regression, Random Forests, and support vector regression (SVR) were established to predict the canopy nitrogen weight from individual multispectral bands and associated vegetation indices (VI). Random Forests using the current techniques/methodologies performed the best out of all the models tested on the validation set with an R2 of 0.85 and Root Mean Square Error (RMSE) of 4.52 g/m2. Adding more spectral variables into the model provided a marginal improvement in the accuracy, while extending the overall processing time. Random Forests provided marginally better results than SVR, but the concepts and analysis are much easier to interpret on Random Forests. Both machine learning models provided a much better accuracy than linear regression. The best model was then applied to the UAV images acquired at different dates for producing maps that show the spatial variation of canopy nitrogen weight within each field at that date.

scholarly journals Online Adaptive Error Compensation SVM-Based Sliding Mode Control of an Unmanned Aerial Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Kaijia Xue ◽  
Congqing Wang ◽  
Zhiyu Li ◽  
Hanxin Chen
Keyword(s):  
Sliding Mode Control ◽  
Unmanned Aerial Vehicle ◽  
Error Compensation ◽  
Sliding Mode ◽  
Control Process ◽  
Support Vector ◽  
Control Effect ◽  
Unknown Parameters ◽  
Mode Control ◽  
Aerial Vehicle

Unmanned Aerial Vehicle (UAV) is a nonlinear dynamic system with uncertainties and noises. Therefore, an appropriate control system has an obligation to ensure the stabilization and navigation of UAV. This paper mainly discusses the control problem of quad-rotor UAV system, which is influenced by unknown parameters and noises. Besides, a sliding mode control based on online adaptive error compensation support vector machine (SVM) is proposed for stabilizing quad-rotor UAV system. Sliding mode controller is established through analyzing quad-rotor dynamics model in which the unknown parameters are computed by offline SVM. During this process, the online adaptive error compensation SVM method is applied in this paper. As modeling errors and noises both exist in the process of flight, the offline SVM one-time mode cannot predict the uncertainties and noises accurately. The control law is adjusted in real-time by introducing new training sample data to online adaptive SVM in the control process, so that the stability and robustness of flight are ensured. It can be demonstrated through the simulation experiments that the UAV that joined online adaptive SVM can track the changing path faster according to its dynamic model. Consequently, the proposed method that is proved has the better control effect in the UAV system.

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

Sensors ◽  
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.

scholarly journals A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen
Keyword(s):  
Differential Evolution ◽  
Texture Analysis ◽  
Concrete Surface ◽  
Image Texture ◽  
Support Vector ◽  
Image Texture Analysis ◽  
Promising Alternative ◽  
Adaptive Differential Evolution ◽  
Hybrid Intelligence ◽  
Intelligence Approach

To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image samples into two categories of no surface void (negative class) and surface void (positive class). Furthermore, to assist the SVM model training phase, the state-of-the-art history-based adaptive differential evolution with linear population size reduction (L-SHADE) is utilized. The hybrid intelligence approach, named as L-SHADE-SVM-SVD, has been developed and complied in Visual C#.NET framework. Experiments with 1000 image samples show that the L-SHADE-SVM-SVD can obtain a high prediction accuracy of roughly 93%. Therefore, the newly developed model can be a promising alternative for construction inspectors in concrete quality assessment.

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