scholarly journals Technology in Indian agriculture - a review

2020 ◽  
Vol 20 (2) ◽  
pp. 1070
Author(s):  
R S Upendra ◽  
I M Umesh ◽  
R B Ravi Varma ◽  
B Basavaprasad
Keyword(s):  
Big Data ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Agricultural Practices ◽  
Advanced Technology ◽  
Economic Losses ◽  
Smart Farming ◽  
Digital Agriculture

Optimization of agricultural practices for enhanced crop yield is considered to be essential phenomena for the countries like India. In order to strengthen the economy and also to meet the food demand for the exponentially growing population, optimizing the agricultural practices has become necessity. In India, weather and geographical conditions are highly variable and were thought to be the major bottleneck of agricultural practices to achieve improved crop yield. Agricultural practices in India are facing many challenges such as change in climatic conditions, different geographical environment, conventional agricultural practices; economic and political scenario. Economic loss due to the lack of information on crop yield productivity is another major concern in the country. These hurdles can be overcome by the implementation of advanced technology in agriculture. Some of the trends observed are smart farming, digital agriculture and Big Data Analytics which provide useful information regarding various crop yields influencing factors and predicting the accurate amounts of crop yield. The exact prediction of crop yield helps formers to develop a suitable cultivation plan, crop health monitoring system, management of crop yield efficiently and also to establish the business strategy in order to decrease economic losses. This also makes the agricultural practices as one of the highly profitable venture. This paper presents insights on the various applications of technology advancements in agriculture such as Digital Agriculture, Smart Farming or Internet of Agriculture Technology (IoAT), Precision Agriculture, Crop Management, Weed and Pest control, Crop protection and Big data analytics.

scholarly journals Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

2018 ◽  
Vol 12 (3) ◽  
pp. 1087 ◽  
Author(s):  
B.M. Sagar ◽  
Cauvery N K
Keyword(s):  
Big Data ◽  
Crop Yield ◽  
Data Analytics ◽  
Crop Production ◽  
Big Data Analytics ◽  
Agricultural Practices ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Main Challenge ◽  
Crop Health

<p>Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of          Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics.  Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.</p>

scholarly journals Intelligent Computational Techniques for Crops Yield Prediction and Fertilizer Management over Big Data Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 3521-3526
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Data Analytics ◽  
Seed Quality ◽  
Big Data Analytics ◽  
Computational Techniques ◽  
Yield Prediction ◽  
Crops Yield

Agriculture is one of the biggest fields to improve the economic rate of the country. Crop yield prediction is a new emerging idea in agriculture. There are several challenges of crops yield prediction in the field of precision agriculture are (i). Obtain minimized production due to climate change; (ii). Lead to different diseases; (iii). Availability of Water; (iv). No awareness of fertilizers and crop features; (v). Climate change; (vi). Unexpected weather events.Other loss factors in the agriculture are lowly seed quality, unplanned irrigation and exploitation of insecticides and fertilizers. The main aim of this research is to design the effective crop yield production and health risk analysis model by big data analytics model. Hence in this research our focus is on optimizing the significant parameters such as rainfall, temperature and fertilizers rate to obtain the P-values for testing the crop and also analyze the human health safety (farmers and suppliers) due to the dynamic change of environment and also soil nutrients. Big data analytics is the feasible platform to test and measure the crop grow in the particular agriculture field. It helps in climate, weather events prediction and also it is used to compute the sufficient resources for crop cultivation.

scholarly journals Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco

2021 ◽  
Vol 24 (1) ◽  
pp. 581
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Support Vector ◽  
Soil Drought ◽  
The Internet ◽  
Data Mining Algorithms ◽  
Smart Farming ◽  
Digital Agriculture ◽  
Mining Algorithms

Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.

scholarly journals A functional framework based on big data analytics for smart farming

2021 ◽  
Vol 24 (3) ◽  
pp. 1772
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
New Products ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Time Production ◽  
Smart Farming ◽  
The Future

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

Big Data and IoT-Allied Challenges Associated With Healthcare Applications in Smart and Automated Systems

2018 ◽  
Vol 9 (2) ◽  
pp. 18-34 ◽  
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Data Analytics ◽  
Smart Cities ◽  
Health Hazard ◽  
Big Data Analytics ◽  
Advanced Technology ◽  
Automated Systems ◽  
Healthcare Applications ◽  
Intelligent Computing

Most of the current smart applications are developed with basis on intelligent computing, most of which are implemented using big data analytics and much other advanced technology. With emerging technology, industrial and instructive improvements are causing greater changes in the lifestyle of people in smart cities and there is more chance of various health problems in urban areas. The way of life in metro urban areas with an expansive volume of people is similarly influenced by different applications and administration frameworks. In this way, the majority of the urban communities are transforming into smart urban areas by receiving mechanized frameworks in every conceivable segment. Therefore, there is more health-related issues and health hazard issues can be identified in urban areas. This article carries out a comprehensive survey of health care issues and improved solutions in automated systems using Big Data Analytics in smart cities integrated with IoT.

scholarly journals N-HyDAA - Big Data Analytics for Malaysia Climate Change Knowledge Management

10.29007/5b3v ◽  
2018 ◽  
Author(s):  
Mohammad Fikry Abdullah ◽  
Mohd Zaki Mat Amin ◽  
Mohd Fauzi Mohamad ◽  
Marini Mohamad Ideris ◽  
Zurina Zainol ◽  
...  
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Economic Losses ◽  
Weather Extremes ◽  
Related Data ◽  
Water Stress Condition ◽  
Business Entities ◽  
Knowledge Portal

With the changing climate, the prognosis is that weather extremes such as floods, drought and EL Nino are likely to increase in frequency and intensity can expand billions of economic losses and effect human lives. NAHRIM Hydroclimate Data Analysis Accelerator (N-HyDAA), known as Malaysia Climate Change (CC) Knowledge Portal, the only CC knowledge portal in Malaysia primarily developed for providing CC and water-related data, information, knowledge and technologywhich is crucial for present and future water related bussines activities, engineering practices and environment. It has eight hydroclimate-environment modules, which amongst others are rainfall, floods, droughts and water stress condition using Big Data Analytics (BDA) technology by means of comprehensive analysis and interactive visualization tools. N-HyDAA is able to trace, detect, identify and visualise future water issues associated with the adverse impacts of climate change in Malaysia. N-HyDAA assist business entities, water operators, engineers, planners and decision-makers in designing, planning and developing water related program and risk management in combating climate change impact either mitigation or adaptation actions.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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