scholarly journals A Smart, Sensible Agriculture System Using the Exponential Moving Average Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 457 ◽  
Author(s):  
Tai-hoon Kim ◽  
Virendra Singh Solanki ◽  
Hardik J. Baraiya ◽  
Anirban Mitra ◽  
Hirav Shah ◽  
...  
Keyword(s):  
Climatic Factors ◽  
Moving Average ◽  
Environmental Parameters ◽  
Cost Effective ◽  
Sensing Element ◽  
Agricultural Modernization ◽  
Modern Agriculture ◽  
Moving Average Model ◽  
Smart Agriculture ◽  
Using Data

Smart agriculture systems with combinations of advanced technologies are used in an attempt to increase the competence of certain farming activities and the standard of living for farm employees by reducing significant labor and tedious tasks. Internet-of-things-based sensors are capable of providing such information about smart agriculture and then acting upon predictions using data analysis. The proposed methodology works alongside a cloud-based server and a mobile-based device (ideally an Android/iOS device) to assist the user in regulating the standing of the plant as monitored by a mix of software packages and hardware devices. Our system detects changes in the moisture, temperature, and light intensity conditions in and around the plant and performs a learning-based call to supply necessary irrigation and illumination to plants. It permits the user to update, manage, and monitor using wireless sensing element networks. The sensors measure the aforementioned parameters and store the data within the cloud, which users can access at any time from anywhere. Farmers will have access to the most up-to-date knowledge so that they can act accordingly and make modifications as needed. This smart planting has become a core tool associated with cost-effective technology in agricultural modernization technologies. The proposed smart modern agriculture tool can be used to monitor climatic factors such as temperature, moisture, and virtually all environmental parameters relevant to the growth of plants.

scholarly journals REVIEW:APPLICATION OF BIG DATA AND IOT PROTECTED MODE IN AGRICULTURE

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.Anita K.Patil ◽  
Dr.A.R. Laware
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Climatic Factors ◽  
Cost Effective ◽  
Big Data Analysis ◽  
Modern Agriculture ◽  
Agriculture Industry ◽  
The Face ◽  
Smart Agriculture ◽  
Protected Mode

Advance researches in the field of Internet of Things (IoT) are helping to make water management smarter and also used for optimizing consumption in the smart agriculture industry. Now days the development and research in Intelligent Smart Farming IoT based devices is turning the face of agriculture production in enhancing as well making it cost-effective and reducing wastage. To create environmental conditions suitable for the growth of animals and plants, modern agriculture that uses artificial techniques to change climatic factors such as temperature a highly efficient protected agriculture mode is used. To handle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. Modern digital technology used for continuously monitoring the physical environment and producing large quantities of data in an unprecedented pace. For improving productivity the analysis of big data would enable farmers and companies to extract value from it. Moreover big data analysis is leading to advances in various industries; it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employs the recent practice of big data analysis, in order to solve various relevant problems.

scholarly journals Statistical Methods for Predicting Malaria Incidences Using Data from Sudan

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hamid H. Hussien ◽  
Fathy H. Eissa ◽  
Khidir E. Awadalla
Keyword(s):  
Time Series ◽  
Malaria Incidence ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Transformation Method ◽  
Transformation Model ◽  
Future Research ◽  
Incidence Data ◽  
Moving Average Model ◽  
Using Data

Malaria is the leading cause of illness and death in Sudan. The entire population is at risk of malaria epidemics with a very high burden on government and population. The usefulness of forecasting methods in predicting the number of future incidences is needed to motivate the development of a system that can predict future incidences. The objective of this paper is to develop applicable and understood time series models and to find out what method can provide better performance to predict future incidences level. We used monthly incidence data collected from five states in Sudan with unstable malaria transmission. We test four methods of the forecast: (1) autoregressive integrated moving average (ARIMA); (2) exponential smoothing; (3) transformation model; and (4) moving average. The result showed that transformation method performed significantly better than the other methods for Gadaref, Gazira, North Kordofan, and Northern, while the moving average model performed significantly better for Khartoum. Future research should combine a number of different and dissimilar methods of time series to improve forecast accuracy with the ultimate aim of developing a simple and useful model for producing reasonably reliable forecasts of the malaria incidence in the study area.

scholarly journals A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

2021 ◽  
Vol 13 (11) ◽  
pp. 5908
Author(s):  
Faris A. Almalki ◽  
Ben Othman Soufiene ◽  
Saeed H. Alsamhi ◽  
Hedi Sakli
Keyword(s):  
Precision Agriculture ◽  
Low Cost ◽  
Environmental Parameters ◽  
Cost Effective ◽  
Crop Productivity ◽  
Environmental Data ◽  
Smart Farming ◽  
Smart Agriculture ◽  
Real Scenario ◽  
The Internet Of Things

When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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