scholarly journals Future Vision, Summary and Outlook

2021 ◽  
pp. 291-296
Author(s):  
Ephrem Habyarimana
Keyword(s):  
Predictive Analytics ◽  
Remote Sensing Data ◽  
Management Approach ◽  
Global Food Security ◽  
Cultivar Development ◽  
Satellite Positioning ◽  
Cropping Season ◽  
Big Data Technologies ◽  
Farming Industry ◽  
Future Vision

AbstractThe DataBio’s agriculture pilots were carried out through a multi-actor whole-farm management approach using information technology, satellite positioning and remote sensing data as well as Internet of Things technology. The goal was to optimize the returns on inputs while reducing environmental impacts and streamlining the CAP monitoring. Novel knowledge was delivered for a more sustainable agriculture in line with the FAO call to achieve global food security and eliminate malnutrition for the more than nine billion people by 2050. The findings from the pilots shed light on the potential of digital agriculture to solve Europe’s concern of the declining workforce in the farming industry as the implemented technologies would help run farms with less workforce and manual labor. The pilot applications of big data technologies included autonomous machinery, mapping of yield, variable rate of applying agricultural inputs, input optimization, crop performance and in-season yields prediction as well as the genomic prediction and selection method allowing to cut cost and duration of cultivar development. The pilots showed their potential to transform agriculture, and the improved predictive analytics is expected to play a fundamental role in the production environment. As AI models are retrained with more data, the decision support systems become more accurate and serve the farmer better, leading to faster adoption. Adoption is further stimulated by cooperation between farmers to share investment costs and technological platforms allowing farmers to benchmark among themselves and across cropping season.

Big-Data-Based Techniques for Predictive Intelligence

2019 ◽  
pp. 1-18
Author(s):  
Dharmpal Singh ◽  
Madhusmita Mishra ◽  
Sudipta Sahana
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Predictive Analytics ◽  
Unstructured Data ◽  
Security Issue ◽  
Operational Costs ◽  
Big Data Technologies ◽  
Ending Process ◽  
Big Data Technology

Big-data-analyzed finding patterns derive meaning and make decisions on data to produce responses to the world with intelligence. It is an emerging area used in business intelligence (BI) for competitive advantage to analyze the structured, semi-structured, and unstructured data stored in different formats. As the big data technology continues to evolve, businesses are turning to predictive intelligence to deepen the engagement to customers with optimization in processes to reduce the operational costs. Predictive intelligence uses sets of advanced technologies that enable organizations to use data stored in real time that move from a historical and descriptive view to a forward-looking perspective of data. The comparison and other security issue of this technology is covered in this book chapter. The combination of big data technology and predictive analytics is sometimes referred to as a never-ending process and has the possibility to deliver significant competitive advantage. This chapter provides an extensive review of literature on big data technologies and its usage in the predictive intelligence.

scholarly journals Personalization as a promise: Can Big Data change the practice of insurance?

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172093514 ◽  
Author(s):  
Laurence Barry ◽  
Arthur Charpentier
Keyword(s):  
Big Data ◽  
Predictive Analytics ◽  
Special Focus ◽  
New Paradigm ◽  
Individual Level ◽  
Big Data Technologies ◽  
State Of Research ◽  
The Individual ◽  
Homogeneity Hypothesis ◽  
The Impact

The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An examination of the current state of research on models with telematics data shows however that the epistemological leap, for now, has not happened.

scholarly journals Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

2022 ◽  
Vol 26 (1) ◽  
pp. 71-89
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Jonas Jägermeyr ◽  
Ann van Griensven
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Global Datasets

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

GML-Based nD Data Management With a Big Geo Data Semantic World Modeling Approach

2018 ◽  
pp. 191-223
Author(s):  
Juergen Rossmann ◽  
Martin Hoppen ◽  
Arno Buecken
Keyword(s):  
Information System ◽  
Data Management ◽  
Data Extraction ◽  
Remote Sensing Data ◽  
Management Approach ◽  
Multiple Time ◽  
Simulation Techniques ◽  
3D Data ◽  
World Modeling ◽  
Forest Simulation

3D simulation applications benefit from realistic and exact forest models. They range from training simulators like flight or harvester simulators to economic and ecological simulations for tree growth or succession. The nD forest simulation and information system integrates the necessary methods for data extraction, modeling, and management of highly realistic models. Using semantic world modeling, tree data can efficiently be extracted from remote sensing data – even for very large areas. Data is modeled using a GML-based modeling language and a flexible data management approach is integrated to provide caching, persistence, a central communication hub, and a versioning mechanism. Combining various simulation techniques and data versioning, the nD forest simulation and information system can provide applications with historic 3D data in multiple time dimensions (hence nD) as well as with predicted data based on simulations.

scholarly journals The use of ALOS/PALSAR data for estimating sugarcane productivity

2014 ◽  
Vol 34 (6) ◽  
pp. 1245-1255 ◽  
Author(s):  
Michelle C. A. Picoli ◽  
Rubens A. C. Lamparelli ◽  
Edson E. Sano ◽  
Jansle V. Rocha
Keyword(s):  
Prediction Models ◽  
Multiple Linear Regression Model ◽  
Remote Sensing Data ◽  
Coefficient Of Determination ◽  
Alos Palsar ◽  
Sar Images ◽  
Long Period ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Sugarcane Productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

scholarly journals Parasitism of Locally Recruited Egg Parasitoids of the Fall Armyworm in Africa

Insects ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 430
Author(s):  
Saidou A. Laminou ◽  
Malick Niango Ba ◽  
Laouali Karimoune ◽  
Ali Doumma ◽  
Rangaswamy Muniappan
Keyword(s):  
Fall Armyworm ◽  
Field Testing ◽  
Egg Parasitoids ◽  
Management Approach ◽  
Egg Masses ◽  
Cropping Season ◽  
Egg Parasitism ◽  
Augmentative Releases ◽  
On Farm ◽  
Telenomus Remus

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is an insect native to the tropical and subtropical Americas that has recently spread to Africa, where it predominately attacks maize, sorghum and other plant species. Biological control is an environmentally friendly way of combatting the pest and contributes to an integrated pest management approach. In Africa, several trichogrammatid parasitoids and Telenomus remus Nixon (Hymenoptera: Platygastridae) have been found parasitizing eggs of the FAW. In Niger, the egg parasitoids encountered include Trichogrammatoidea sp. (Hymenoptera: Trichogrammatidae) and Telenomus remus Nixon. Parasitism of the FAW eggs by the two egg parasitoids was assessed in the laboratory, followed by field testing on sentinel eggs. In the laboratory, T. remus parasitized on average 78% of FAW eggs, compared to 25% for Trichogrammatoidea sp. Telenomus remus was able to parasitize egg masses that were fully covered with scales, while Trichogrammatoidea sp. parasitized only uncovered egg masses. On-farm releases of T. remus in sorghum fields caused up to 64% of FAW egg parasitism. Parasitized eggs yielded viable progeny, which can contribute to FAW egg parasitism build-up during the cropping season. Our findings lay the groundwork for the use of T. remus in augmentative releases against FAW in Africa.

scholarly journals Sustainable Land Use Management Needed to Conserve the Dragon’s Blood Tree of Socotra Island, a Vulnerable Endemic Umbrella Species

2019 ◽  
Vol 11 (13) ◽  
pp. 3557 ◽  
Author(s):  
Petr Maděra ◽  
Daniel Volařík ◽  
Zdeněk Patočka ◽  
Hana Kalivodová ◽  
Josef Divín ◽  
...  
Keyword(s):  
Population Size ◽  
Community Forestry ◽  
Conservation Status ◽  
Remote Sensing Data ◽  
Sustainable Land Use ◽  
Projection Area ◽  
Management Approach ◽  
Extinction Time ◽  
Dragon’S Blood ◽  
Socotra Island

Unsustainable overgrazing is one of the most important threats to the endemic and endangered population of dragon’s blood tree (Dracaena cinnabari) on Socotra Island (Republic of Yemen). However, there is a lack of information about the exact population size and its conservation status. We estimated the population size of D. cinnabari using remote sensing data. The age structure was inferred using a relationship between crown projection area and the number of branch sections. The conservation importance of each sub-population was assessed using a specially developed index. Finally, the future population development (extinction time) was predicted using population matrices. The total population size estimated consists of 80,134 individuals with sub-populations varying from 14 to 32,196 individuals, with an extinction time ranging from 31 to 564 years. Community forestry controlled by a local certification system is suggested as a sustainable land management approach providing traditional and new benefits and enabling the reforestation of endemic tree species on Socotra Island.

Advanced Threat Detection Based on Big Data Technologies

2018 ◽  
pp. 1-19
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

Advanced Threat Detection Based on Big Data Technologies

2020 ◽  
pp. 808-822
Author(s):  
Madhvaraj M. Shetty ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Threat Detection ◽  
Cyber Threats ◽  
Threat Intelligence ◽  
Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

scholarly journals Improved Representation of Agricultural Land Use and Crop Management for Large Scale Hydrological Impact Simulation in Africa using SWAT+

2021 ◽  
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Jonas Jägermeyr ◽  
Ann van Griensven
Keyword(s):  
Management Practices ◽  
Agricultural Land ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Global Datasets

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). The SWAT+ model represents agricultural land by default in a generic way where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for Northeast Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural Hydrologic Response Units (HRUs) which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in regional to global hydrological models.

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