The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and ...machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.
A review on deep learning in UAV remote sensing Osco, Lucas Prado; Marcato Junior, José; Marques Ramos, Ana Paula ...
International journal of applied earth observation and geoinformation,
October 2021, 2021-10-00, 2021-10-01, Letnik:
102
Journal Article
Recenzirano
Odprti dostop
•Combining deep learning and UAV-based data is an emerging trend in remote sensing.•Most articles published rely on CNN-based methods.•Future perspectives in UAV-based data processing still have much ...to cover.
Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms’ applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicle (UAV)-based applications have dominated aerial sensing research. However, a literature revision that combines both “deep learning” and “UAV remote sensing” thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing the classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published materials and evaluated their characteristics regarding the application, sensor, and technique used. We discuss how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. This revision consisting of an approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.
Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great ...potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees.
Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the ...comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as
Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.
We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 ...labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages.
Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of ...individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.
Syntactic methods in computer vision represent visual patterns in a hierarchical and compositional perspective, which is converted to strings. Long short-term memory (LSTM) is able to learn patterns ...in sequences. In this letter, we propose a syntactic approach to represent visual patterns as sequences of symbols, and we use an LSTM as a classifier to learn the relationship between the symbols in sequences. An extensive experimental evaluation using aerial images from a soybean field captured by unmanned aerial vehicles has been conducted to compare our method with two deep learning architectures, one syntactic method, and one shallow learning algorithm. The results achieved by the proposed method maintain stability even when trained on small data sets, suggesting that representing visual patterns in a compositional way, repeating primitives, may be a viable alternative when there are only a limited number of samples for training.
The Dendrocephalus brasiliensis, a native species from South America, is a freshwater crustacean well explored in conservational and productive activities. Its main characteristics are its rusticity ...and resistance cysts production, in which the hatching requires a period of dehydration. Independent of the species utilization nature, it is essential to manipulate its cysts, such as the counting using microscopes. Manually counting is a difficult task, prone to errors, and that also very time-consuming. In this paper, we propose an automatized approach for the detection and counting of Dendrocephalus brasiliensis cysts from images captured by a digital microscope. For this purpose, we built the DBrasiliensis dataset, a repository with 246 images containing 5141 cysts of Dendrocephalus brasiliensis. Then, we trained two state-of-the-art object detection methods, YOLOv3 (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks), on DBrasiliensis dataset in order to compare them under both cyst detection and counting tasks. Experiments showed evidence that YOLOv3 is superior to Faster R-CNN, achieving an accuracy rate of 83,74%, R2 of 0.88, RMSE (Root Mean Square Error) of 3.49, and MAE (Mean Absolute Error) of 2.24 on cyst detection and counting. Moreover, we showed that is possible to infer the number of cysts of a substrate, with known weight, by performing the automated counting of some of its samples. In conclusion, the proposed approach using YOLOv3 is adequate to detect and count Dendrocephalus brasiliensis cysts. The DBrasiliensis dataset can be accessed at: https://doi.org/10.6084/m9.figshare.13073240.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Apesar da popularização do uso de aeronaves remotamente pilotadas (RPA) como ferramentas para produção de produtos fotogramétricos e cartográficos digitais, pouco se fala a respeito da acurácia de ...tais produtos no contexto de terrenos acidentados, onde a diferença abrupta de altitudes gera uma maior complexidade na modelagem do relevo e consequentemente na geração das ortofotos. O objetivo desse trabalho é apresentar a avaliação e classificação baseado no PEC-PCD (Padrão de Exatidão Cartográfica para Produtos Cartográficos Digitais) de ortofotomosaicos e modelos digitais de superfície (DSM) gerados para uma mesma área de mineração. Para esse estudo utilizamos imagens RGB (não métrica) com GSD (Ground Sample Distance) estimado de 2,45 cm, e sobreposição de 80%/80%, captadas por RPA do tipo multirotor em dois voos idênticos realizados em datas distintas, para cada voo foram utilizados 15 alvos pré-sinalizados dos quais foram coletadas as coordenadas X, Y e Z com auxilio de equipamento GNSS RTK. Cinco experimentos foram realizados, variando o número de GCP (Ground Control Points) e mantendo o número de CP (Check Points). Os produtos (ortofotomosaicos e DSM) gerados com as diferentes configurações de GCP, foram avaliados com base no PEC-PCD e, analisando os resultados obtidos foi possível constatar a variação de escala na qual os produtos se enquadram, esse fato foi atribuído à quantidade e disposição (geometria) dos GCP. De forma geral, os produtos gerados com 6 e 8 GCP apresentaram níveis de acurácia semelhantes entre si e foram classificados como Classe A para a escala 1:1000.
Time series forecasting is the process of predicting future values of a time series from knowledge of its past data. Although there are several models for making short-term predictions, the problem ...of long temporal sequences can still receive new contributions. Recent studies have applied Transformers-based solutions to the long-time series forecasting task and achieved good results. When it comes to time series modeling, the goal is to capture the temporal relationships within a sequence of ordered and continuous points. While using positional encoding and embedding sub-series as tokens in Transformers can help to preserve some ordering information, the permutation-invariant self-attention mechanism used in these models can lead to loss of temporal information. In this paper, we used convolutional networks in a binary tree structure with skip connections between the levels of the tree that allowed greater precision and efficiency in the training. The proposed model was evaluated on five real-life datasets. Experimental results show that our model significantly improves forecast accuracy relative to existing solutions.
•We propose a new method for predicting Long sequence time-series.•Using skip connections to improve the convergence of the model.•Results using the proposed method proved superior to other approaches.
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