scholarly journals VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map

2020 ◽  
Vol 12 (20) ◽  
pp. 3305
Author(s):  
Mohamed Kerkech ◽  
Adel Hafiane ◽  
Raphael Canals
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Depth Map ◽  
Semantic Segmentation ◽  
Learning Technologies ◽  
Disease Detection ◽  
Registration Method ◽  
Multispectral Data ◽  
3D Processing ◽  
Uav Images

Vine pathologies generate several economic and environmental problems, causing serious difficulties for the viticultural activity. The early detection of vine disease can significantly improve the control of vine diseases and avoid spread of virus or fungi. Currently, remote sensing and artificial intelligence technologies are emerging in the field of precision agriculture. They offer interesting potential for crop disease management. However, despite the advances in these technologies, particularly deep learning technologies, many problems still present considerable challenges, such as semantic segmentation of images for disease mapping. In this paper, we present a new deep learning architecture called Vine Disease Detection Network (VddNet). It is based on three parallel auto-encoders integrating different information (i.e., visible, infrared and depth). Then, the decoder reconstructs and retrieves the features, and assigns a class to each output pixel. An orthophotos registration method is also proposed to align the three types of images and enable the processing by VddNet. The proposed architecture is assessed by comparing it with the most known architectures: SegNet, U-Net, DeepLabv3+ and PSPNet. The deep learning architectures were trained on multispectral data from an unmanned aerial vehicle (UAV) and depth map information extracted from 3D processing. The results of the proposed architecture show that the VddNet architecture achieves higher scores than the baseline methods. Moreover, this study demonstrates that the proposed method has many advantages compared to methods that directly use the UAV images.

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 A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3921 ◽  
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Yinghua Ye ◽  
Peerapong Torteeka ◽  
Kritanai Torsri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Building Detection ◽  
Network Training ◽  
Building Information ◽  
Open Standard ◽  
Uav Images ◽  
Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

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 Study on the Detection of Cattle in UAV Images Using Deep Learning

2019 ◽  
Author(s):  
Jayme Garcia Arnal Barbedo ◽  
Luciano Vieira Koenigkan ◽  
Thiago Teixeira Santos ◽  
Patrícia Menezes Santos
Keyword(s):  
Deep Learning ◽  
Bos Taurus ◽  
Relevant Information ◽  
Learning Technologies ◽  
Aerial Images ◽  
Ground Sample ◽  
Bos Taurus Indicus ◽  
Uav Images ◽  
Production Areas ◽  
Animal Detection

Unmanned Aerial Vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and Convolutional Neural Networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: 1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (\textit{Bos taurus indicus}); 2) to determine the ideal Ground Sample Distance (GSD) for animal detection; 3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1,853 images containing 8,629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures * 3 spacial resolutions * 2 datasets * 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

scholarly journals A Study on the Detection of Cattle in UAV Images Using Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5436 ◽  
Author(s):  
Jayme Garcia Arnal Barbedo ◽  
Luciano Vieira Koenigkan ◽  
Thiago Teixeira Santos ◽  
Patrícia Menezes Santos
Keyword(s):  
Deep Learning ◽  
Bos Taurus ◽  
Relevant Information ◽  
Learning Technologies ◽  
Aerial Images ◽  
Ground Sample ◽  
Bos Taurus Indicus ◽  
Uav Images ◽  
Production Areas ◽  
Animal Detection

Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (Bos taurus indicus); (2) to determine the ideal ground sample distance (GSD) for animal detection; (3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1853 images containing 8629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures × 3 spacial resolutions × 2 datasets × 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

scholarly journals Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1690 ◽  
Author(s):  
M Bah ◽  
Adel Hafiane ◽  
Raphael Canals
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Training Data ◽  
Training Dataset ◽  
Second Phase ◽  
Agricultural Field ◽  
Classification Problems ◽  
Weed Detection ◽  
Uav Images ◽  
The Right

In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.

scholarly journals MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Danilo Avola ◽  
Daniele Pannone
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Low Cost ◽  
Semantic Segmentation ◽  
Search And Rescue ◽  
Small Scale ◽  
Border Control ◽  
Control Precision ◽  
Challenging Tasks ◽  
Deep Learning Model

In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method.

scholarly journals An Comprehensive Survey on Applications of Precision Agriculture in the Context of Weed Classification, Leave Disease Detection, Yield Prediction and UAV Image Analysis

2021 ◽  
Author(s):  
Preethi C ◽  
Brintha NC ◽  
Yogesh CK
Keyword(s):  
Image Analysis ◽  
Precision Agriculture ◽  
The State ◽  
Disease Detection ◽  
Yield Prediction ◽  
Comprehensive Survey ◽  
Uav Images ◽  
Uav Image ◽  
Image Analysis Techniques ◽  
State Of Art

Advancement in technologies such as Machine vision, Machine Learning, Deep Learning algorithms enables them to extend its horizon in different applications including precision agriculture. The objective of this work is to study the various works pertaining to precision agriculture under four categories, weed classification, disease detection in leaves, yield prediction and image analysis techniques in UAV. In case of the weed classification, both classifying weeds from the crops and classifying the different types of weeds are analysed. In disease detection, only the diseases that occur in the leaves of different plants are considered and studied. It is continued with the state of art models that predicts yields of different crops. The last part of the work concentrates on analysing the images captured UAV in the context of precision agriculture. This work would pave a way for getting a deep insight about the state of art models related to the above specified applications of precision agriculture and the methods of analysing the UAV images.

scholarly journals A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

2019 ◽  
Vol 11 (24) ◽  
pp. 2939 ◽  
Author(s):  
Lonesome Malambo ◽  
Sorin Popescu ◽  
Nian-Wei Ku ◽  
William Rooney ◽  
Tan Zhou ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Precision Agriculture ◽  
Crop Improvement ◽  
Image Data ◽  
Semantic Segmentation ◽  
Detection Accuracy ◽  
Phenotypic Data ◽  
Field Level

Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficiency in image capture is leading to massive datasets, which pose analysis challenges in providing needed phenotypic data. To complement these high-throughput platforms, there is an increasing need in crop improvement to develop robust image analysis methods to analyze large amount of image data. Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets. This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum crop improvement, from UAS images over selected sorghum experimental plots. The SegNet model was trained to semantically segment UAS images into sorghum panicles, foliage and the exposed ground using 462, 250 × 250 labeled images, which was then applied to field orthomosaic to generate a field-level semantic segmentation. Individual panicle locations were obtained after post-processing the segmentation output to remove small objects and split merged panicles. A comparison between model panicle count estimates and manually digitized panicle locations in 60 randomly selected plots showed an overall detection accuracy of 94%. A per-plot panicle count comparison also showed high agreement between estimated and reference panicle counts (Spearman correlation ρ = 0.88, mean bias = 0.65). Misclassifications of panicles during the semantic segmentation step and mosaicking errors in the field orthomosaic contributed mainly to panicle detection errors. Overall, the approach based on deep learning semantic segmentation showed good promise and with a larger labeled dataset and extensive hyper-parameter tuning, should provide even more robust and effective characterization of sorghum panicle counts.

scholarly journals Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model

2021 ◽  
Vol 10 (3) ◽  
pp. 144
Author(s):  
Asmamaw A Gebrehiwot ◽  
Leila Hashemi-Beni
Keyword(s):  
Deep Learning ◽  
Water Depth ◽  
Water Surface ◽  
Point Cloud ◽  
Depth Map ◽  
Depth Estimation ◽  
Surface Model ◽  
Topographic Data ◽  
Inundation Depth ◽  
Uav Images

Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively.

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