Progress in detecting and mapping crop diseases, with particular reference to maize using remote sensing.

2021 ◽  
Vol 16 (041) ◽  
Author(s):  
Onisimo Mutanga
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Data ◽  
Disease Classification ◽  
Disease Spread ◽  
Remotely Sensed Data ◽  
Maize Crop ◽  
Large Size ◽  
Crop Diseases ◽  
Crop Disease

Abstract Disease infection on crops has been increasing over the years, in line with the changing climate, which has provided a conducive environment for disease proliferation. Timely and up-to-date information on disease spread and its magnitude is a critical component of crop management. This study provides a detailed overview on the role of remote sensing in crop disease detection and mapping with particular reference to the implication on maize, a staple food for many countries in the Global South. Studies have shown the capability of various remote sensing approaches in detecting the severity of disease infection. Most studies undertaken focused on disease classification, with hyperspectral data demonstrating satisfactory performance in detecting the early stages of disease infection. Thermal remote sensing has great potential but remains largely unexplored and very few studies have focused on the application of remote sensing on maize crop diseases in different environments. With new developments on unmanned aerial vehicles (AUVs), there is a great potential to mount sensors with useful information for precise crop disease monitoring and the large size and architecture of maize leaves provide opportunities for early detection with high-resolution remotely sensed data.

scholarly journals The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1615 ◽  
Author(s):  
Dejuan Jiang ◽  
Kun Wang
Keyword(s):  
Remote Sensing ◽  
Human Activities ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Machine Learning Techniques ◽  
Remotely Sensed Data ◽  
Coupled Models

A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling.

A Review on Rice Crop Disease Classification Using Computational Approach

2021 ◽  
Author(s):  
V. Malathi ◽  
M. P. Gopinath
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Disease Classification ◽  
Rice Crop ◽  
Support Vector ◽  
Processing Machine ◽  
Crop Diseases ◽  
Crop Disease

Rice is a significant cereal crop across the world. In rice cultivation, different types of sowing methods are followed, and thus bring in issues regarding sampling collection. Climate, soil, water level, and a diversified variety of crop seeds (hybrid and traditional varieties) and the period of growth are some of the challenges. This survey mainly focuses on rice crop diseases which affect the parts namely leaves, stems, roots, and spikelet; it mainly focuses on leaf-based diseases. Existing methods for diagnosing leaf disease include statistical approaches, data mining, image processing, machine learning, and deep learning techniques. This review mainly addresses diseases of the rice crop, a framework to diagnose rice crop diseases, and computational approaches in Image Processing, Machine Learning, Deep Learning, and Convolutional Neural Networks. Based on performance indicators, interpretations were made for the following algorithms namely support vector machine (SVM), convolutional neural network (CNN), backpropagational neural network (BPNN), and feedforward neural network (FFNN).

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

Central ossifying fibroma of mandible

2020 ◽  
Vol 13 (12) ◽  
pp. e239286
Author(s):  
Kumar Nilesh ◽  
Prashant Punde ◽  
Nitin Shivajirao Patil ◽  
Amol Gautam
Keyword(s):  
Fibrous Tissue ◽  
Three Dimensional ◽  
Facial Asymmetry ◽  
Ossifying Fibroma ◽  
Mandible Reconstruction ◽  
Three Dimensional Printing ◽  
Osseous Lesion ◽  
Large Size ◽  
Dimensional Printing

Ossifying fibroma (OF) is a rare, benign, fibro-osseous lesion of the jawbone characterised by replacement of the normal bone with fibrous tissue. The fibrous tissue shows varying amount of calcified structures resembling bone and/or cementum. The central variant of OF is rare, and shows predilection for mandible among the jawbone. Although it is classified as fibro-osseous lesion, it clinically behaves as a benign tumour and can grow to large size, causing bony swelling and facial asymmetry. This paper reports a case of large central OF of mandible in a 40-year-old male patient. The lesion was treated by segmental resection of mandible. Reconstruction of the surgical defect was done using avascular fibula bone graft. Role of three-dimensional printing of jaw and its benefits in surgical planning and reconstruction are also highlighted.

scholarly journals The Role of Risk Preferences in Responses to Messaging About COVID-19 Vaccine Take-Up

2021 ◽  
pp. 194855062199962
Author(s):  
Jennifer S. Trueblood ◽  
Abigail B. Sussman ◽  
Daniel O’Leary
Keyword(s):  
Large Scale ◽  
Risk Preferences ◽  
Herd Immunity ◽  
Disease Spread ◽  
Short Term ◽  
Policy Makers ◽  
Health Crisis ◽  
Flu Vaccine ◽  
Nationally Representative

Development of an effective COVID-19 vaccine is widely considered as one of the best paths to ending the current health crisis. While the ability to distribute a vaccine in the short-term remains uncertain, the availability of a vaccine alone will not be sufficient to stop disease spread. Instead, policy makers will need to overcome the additional hurdle of rapid widespread adoption. In a large-scale nationally representative survey ( N = 34,200), the current work identifies monetary risk preferences as a correlate of take-up of an anticipated COVID-19 vaccine. A complementary experiment ( N = 1,003) leverages this insight to create effective messaging encouraging vaccine take-up. Individual differences in risk preferences moderate responses to messaging that provides benchmarks for vaccine efficacy (by comparing it to the flu vaccine), while messaging that describes pro-social benefits of vaccination (specifically herd immunity) speeds vaccine take-up irrespective of risk preferences. Findings suggest that policy makers should consider risk preferences when targeting vaccine-related communications.

scholarly journals Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4886
Author(s):  
Shilei Li ◽  
Maofang Gao ◽  
Zhao-Liang Li
Keyword(s):  
Chlorophyll Fluorescence ◽  
Fluorescence Spectrum ◽  
Singular Vector ◽  
Low Frequency ◽  
Information Criterion ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Remotely Sensed Data ◽  
Spectral Window ◽  
Vector Decomposition

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.

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