Agricultural Systems Management Technologies for Precision Agriculture

2002 ◽  
Author(s):  
Gaines E. Miles ◽  
Daniel R. Ess ◽  
R. Mack Strickland ◽  
Mark T. Morgan
Keyword(s):  
Precision Agriculture ◽  
Agricultural Systems ◽  
Systems Management

Climate risks are spurring innovation in agriculture

2017 ◽  
Keyword(s):  
Precision Agriculture ◽  
Commodity Markets ◽  
Insurance Markets ◽  
Agricultural Systems ◽  
Content Type ◽  
Climate Risks ◽  
Central Eastern Europe ◽  
Market Participants ◽  
The World ◽  
Forward Prices

Subject Innovation in agriculture. Significance Climate change is increasing weather variability and is already affecting the production levels of major agricultural systems across the world. From June to September 2017, Central-Eastern Europe experienced drought conditions that affected several crops. Forward prices in commodity markets have risen, as commercial and speculative market participants protect themselves from climate risks. Impacts Risk management will be increasingly proactive, in all parts of the supply chain. Greater use of insurance markets, and the more sophisticated range of cover available, will give farmers more income stability. Precision agriculture, or satellite farming, will increasingly be adopted by farmers, improving their management of risks.

scholarly journals Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽  
2018 ◽  
Vol 2018 (6) ◽  
Author(s):  
Yiannis Ampatzidis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Agricultural Systems ◽  
Disease Identification ◽  
Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

Agricultural hydroinformatics: agricultural water systems management as a new application for hydroinformatics

2020 ◽  
Author(s):  
Paul Celicourt ◽  
Silvio J. Gumiere ◽  
Alain Rousseau
Keyword(s):  
Agricultural Development ◽  
Environmental Science ◽  
Simulation Models ◽  
Water Systems ◽  
Agricultural Systems ◽  
Agricultural Water ◽  
Systems Management ◽  
Research Areas ◽  
Systems Research ◽  
Wide Range

<p>Hydroinformatics, throughout its more than 25 years of existence, has been applied to a set of research areas. So far, these applications include: hydraulics and hydrology, environmental science and technology, knowledge systems and knowledge management, urban water systems management.</p><p>This paper introduces agricultural water systems management as a new application for hydroinformatics, and terms it as “agricultural hydroinformatics”. It presents a discipline-delineated conceptual framework originating from the particularities of the socio-technical dimension of applying hydroinformatics in agriculture. It epitomizes the wholeness and inter-dependencies of agricultural systems studies and modelling. It is suitable to support, not only integrated agricultural water resources management in particular, but also agricultural sustainability in general, in addition to a wide range of agricultural development situations beyond connections between agro-economic and water engineering development and its socio-economic impacts.</p><p>The paper also highlights some contributions of hydroinformatics to agriculture including new kinds of sensing technologies, information and simulation models development that bear the potential to boost reproducibility of agricultural systems research through systematic and formal records of the relationships among raw data, the processes that produce results and the results themselves.</p>

scholarly journals Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping

2020 ◽  
Vol 12 (7) ◽  
pp. 1116 ◽  
Author(s):  
Onur Yuzugullu ◽  
Frank Lorenz ◽  
Peter Fröhlich ◽  
Frank Liebisch
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Soil Property ◽  
Clay Content ◽  
Agricultural Systems ◽  
Property Prediction ◽  
High Agreement ◽  
New Era ◽  
Field Management ◽  
Agronomic Yield

Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.

Spectral Monitoring of the Crops Vegetation Status in Precision Agriculture

2015 ◽  
Vol 811 ◽  
pp. 236-240
Author(s):  
Marius Cristian Luculescu ◽  
Luciana Cristea ◽  
Sorin Constantin Zamfira ◽  
Ion Barbu
Keyword(s):  
Complex System ◽  
Environmental Protection ◽  
Spectral Data ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Agricultural Systems ◽  
Efficient System ◽  
The Status ◽  
Data Acquisition And Processing ◽  
Spectral Monitoring

This paper proposes an analysis of spectral monitoring processes of the crop vegetation status. In terms of the extensive implementation of precision agriculture, the performant agricultural management must ensure the monitoring of crop vegetation. In this context, determination and interpretation of vegetation indices, based on spectral data, plays a very important role. The performed research revealed the characteristics of monitoring the status of vegetation in order to obtain the necessary information, ie structure, actions and performances required to achieve an efficient system of monitoring the state of vegetation resources and to obtain the application maps for a precision crop management.precision crop management. This complex system ensures data acquisition and processing, thematic and application maps realization and decision generating on obtaining large productions and quality, on optimizing the economic profits, achieving of an integrated environmental protection and increasing of the sustainability of agricultural systems.

Sign in / Sign up

Export Citation Format

Share Document