Predicting wildfire spread behavior is an extremely important task for many countries. On a small scale, it possible to ensure constant monitoring of the natural landscape through ground means. However, on scale large countries, this becomes practically impossible due remote and vast forest territories. The most promising source data in case that can provide global sensing data. Currently, main challenge development effective pipeline combines geospatial collection application advanced...

Artificial intelligence (AI) has smoothly penetrated in a number of monitoring and control applications including agriculture. However, research efforts toward low-power sensing devices with fully functional AI on board are still fragmented. In this article, we present an embedded system enriched the AI, ensuring continuous analysis situ prediction growth dynamics plant leaves. The solution is grounded graphics processing unit (GPU) able to run neural network-based board. We use recurrent...

We present Internet of Things (IoT) deployment in a tomato greenhouse Russia. The IoT enabling technologies applied for this comprise wireless sensor network, cloud computing, and artificial intelligence. They are to help monitoring controlling both plants conditions as well predicting the growth rate tomatoes.

Artificial Intelligence (AI) has been recently applied to a number of sensing scenarios for realizing the prediction, control and/or recognition tasks. However, its integration embedded systems is still limited. We propose low-power system with AI on board special focus application in agriculture. For this reason we designed Convolutional Neural Network (CNN) which achieves 83% average Intersection over Union (IoU) score test dataset and 97% seeds accuracy validation dataset. The proposed...

In this article, we share our experience in the scope of controlled-environment agriculture automation Antarctic station greenhouse facility called EDEN ISS. For remote plant monitoring, control, and maintenance, solve problem classification. Due to inherent communication limitations between Antarctica Europe, first propose image compression mechanism for data collection. We show that can compress images, on average, 7.2 times efficient transmission over weak channel. Moreover, prove...

The Hogweed of Sosnowskyi (lat. Heracleum sosnówskyi) is poisonous for humans, dangerous farming crops, and local ecosystems. This plant fast-growing has already spread all over Eurasia: from Germany to the Siberian part Russia, its distribution expands year-by-year. In-situ detection this harmful a tremendous challenge many countries. Meanwhile, there are no automatic systems localization hogweed. In article, we report on an approach fast accurate includes Unmanned Aerial Vehicle (UAV) with...

The canopy height model (CHM) is a representation of the top vegetation from surrounding ground level. It crucial for extraction various forest characteristics, instance, timber stock estimations and growth measurements. There are different ways obtaining height, such as through ground-based observations or interpretation remote sensing images. severe downside field measurement its cost acquisition difficulty. Therefore, utilizing data is, in many cases, preferable. enormous advances...

Satellite data allows us to solve a wide range of challenging tasks remotely, including monitoring changing environmental conditions, assessing resources, and evaluating hazards. Computer vision algorithms such as convolutional neural networks have proven be powerful tools for handling huge visual datasets. Although the number satellite imagery is constantly growing artificial intelligence advancing, present sticking point in remote sensing studies quality amount annotated Typically, manual...

Remote sensing of forests is a powerful tool for monitoring the biodiversity ecosystems, maintaining general planning, and accounting resources. Various sensors bring together heterogeneous data, advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration environmental studies include dominant species, tree age, height, basal area timber stock. Being proxies stand productivity, they can be utilized carbon stock...

Estimation of terrestrial carbon balance is one the key tasks in understanding and prognosis climate change impacts development tools policies according to mitigation adaptation strategies. Forest ecosystems are major pools stocks affected by controversial processes influencing stability. Therefore, monitoring forest a proper inventory management resources planning their sustainable use. In this survey, we discuss which computer vision techniques applicable most important aspects actions,...

In the context of global climate change and rising anthropogenic loads, outbreaks both endemic invasive pests, pathogens, diseases pose an increasing threat to health, resilience, productivity natural forests forest plantations worldwide. The effective management such threats depends on opportunity for early-stage action helping limit damage expand, which is difficult implement large territories. Recognition technologies based analysis Earth observation data are basis tools monitoring spread...

Abstract This research aims to establish the possible habitat suitability of Heracleum sosnowskyi ( HS ), one most aggressive invasive plants, in current and future climate conditions across territory European part Russia. We utilised a species distribution modelling framework using publicly available data plant occurrence collected citizen science projects CSP ). Climatic variables soil characteristics were considered follow dependencies with environmental factors. applied Random Forest...

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis the environment. The remote sensing domain provides vast amounts satellite data, but it also poses challenges associated with processing this data. Baseline solutions intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, emergency situation Despite these advances, two major issues high-performing...

Wildfires play a pivotal role in environmental processes and the sustainable development of ecosystems. Timely responses can significantly reduce damages consequences caused by their spread. Several critical issues wildfire behavior analysis include fire occurrence forecasting, early detection, spread prediction. In this study, we focus on which is valuable tool for facilitating earlier intervention. Conventional approaches primarily rely computation indices based weather conditions....

Earth remote sensing data can be applied to detect and assess the condition of infrastructure objects on vast territories. One such object is electric pylons, which ensure sustainability energy supply in rural urban areas. In some regions, power lines damaged by natural hazards as earthquakes, strong winds, or floods. Currently, main limitation developing highly effective algorithms for utilities assessment associated with availability diverse environmental conditions. Therefore, this study,...

Sustainable management of the environment is based on preservation natural resources, first all, freshwater—both surface and groundwater—from exhaustion contamination. Thus, development adequate monitoring solutions, including fast adaptive modelling approaches, are high importance. Recent progress in machine learning techniques provide an opportunity to improve prediction accuracy spatial distribution properties objects automate all stages this process exclude uncertainties caused by...

Usage of multispectral satellite imaging data opens vast possibilities for monitoring and quantitatively assessing properties or objects interest on a global scale. Machine learning computer vision (CV) approaches show themselves as promising tools automatizing image analysis. However, there are limitations in using CV data. Mainly, the crucial one is amount available model training. This paper presents novel augmentation approach called MixChannel that helps to address this limitation...

Deep convolutional neural networks are highly efficient for computer vision tasks using plenty of training data. However, there remains a problem small datasets. For addressing this the pipeline which handles rare object types and an overall lack data to build well-performing models that provide stable predictions is required. This article reports on comprehensive framework <i>XtremeAugment</i> provides easy, reliable, scalable way collect image datasets efficiently label augment collected...

Food quality control is an important task in the agricultural domain at postharvest stage for avoiding food losses. The latest achievements image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on colorization image-to-image translation plant stage. In this article, we propose approach Generative Adversarial Network (GAN) Convolutional Neural (CNN) techniques to use synthesized segmented VNIR imaging data early decay fungal...

Large datasets catalyze the rapid expansion of deep learning and computer vision. At same time, in many domains, there is a lack training data, which may become an obstacle for practical application vision models. To overcome this problem, it popular to apply image augmentation. When dataset contains instance segmentation masks, possible instance-level It operates by cutting from original pasting new backgrounds. This article challenges with objects present various domains. We introduce...

Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct rapid accurate assessment damage take measures restore critical infrastructure as quickly possible. Remote sensing monitoring using artificial intelligence promising tool for estimating extent flooded areas. However, flood still presents some challenges due varying weather conditions cloud cover limit use visible satellite data....

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite usefulness NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make possible generate artificial information, example, solve colorization problem. In this research, we aim investigate whether approach can produce only...