scholarly journals Simulation and Prediction of Rainfall and Crop Yield in West Java Using ANFIS

2017 ◽  
Vol 13 (2) ◽  
pp. 83
Author(s):  
Ruminta Roem ◽  
Tati Nurmala

Simulation of numerical data for prediction purposes is very important for the planning and anticipation of the future, for example, prediction data of rainfall and agricultural production. There are various models to simulate and forecast the numerical data, one of which is a artificial intelligence model using ANFIS. In this connection it has studied a simulation and prediction of rainfall and agricultural production in West Java using ANFIS. The study uses data of rainfall and crop production. The method of this study is descriptive explanatory which is a type of quantitative analysis. Numerical data were analyzed using ANFIS of the Software Matlab 8.0. The study results showed that ANFIS can simulate rainfall and crop yield with highly accurate and has the potential to be used as one of the alternative model to predict rainfall and crop yield in West Java

2017 ◽  
Vol 13 (2) ◽  
pp. 83
Author(s):  
Ruminta Roem ◽  
Tati Nurmala

Simulation of numerical data for prediction purposes is very important for the planning and anticipation of the future, for example, prediction data of rainfall and agricultural production. There are various models to simulate and forecast the numerical data, one of which is a artificial intelligence model using ANFIS. In this connection it has studied a simulation and prediction of rainfall and agricultural production in West Java using ANFIS. The study uses data of rainfall and crop production. The method of this study is descriptive explanatory which is a type of quantitative analysis. Numerical data were analyzed using ANFIS of the Software Matlab 8.0. The study results showed that ANFIS can simulate rainfall and crop yield with highly accurate and has the potential to be used as one of the alternative model to predict rainfall and crop yield in West Java


2020 ◽  
Vol 222 ◽  
pp. 06023
Author(s):  
Elena Kolomeeva ◽  
Anna Kharitonova ◽  
Natalia Zaruk

The article contains regions differentiation on competitiveness of agricultural production taking into account meteorological conditions. Regions with favorable and unfavorable conditions of agriculture were identified. The direct competitiveness dependence of subjects of the Russian Federation on the climate factor is revealed; in the future it will allow to determine ways of increasing productivity and efficiency of agricultural activities and will increase the competitiveness of individual regions. In each group, the most and least competitive subjects for crop production and livestock are identified.


Afrika Focus ◽  
1990 ◽  
Vol 6 (2) ◽  
pp. 141-155
Author(s):  
Paul Vossen

The interannual variability of traditional, rainfed agricultural production of Botswana, a country with a typical semi-arid climate, is almost completely accounted for by the quality of the rainy season. It appears that the variability of the national cattle death ratio, total planted area and crop yield are, for more than 95% accounted for by rainy season conditions. As a result, also the nutritional state of the population highly correlates with rainfall. Despite the severe droughts of 1978/79and1985/86, farmers were not discouraged to practice agriculture: in fact, crop production shows a significant positive time trend which becomes apparent, when the trend and the rainy season conditions are analysed in combination with each other. As part of this study, models were developed and validated for a precise and areawise agricultural rainy season quality monitoring and for national agricultural production forecasting in Botswana. One of these models could possibly also be used for the areawise assessment of risks for malnutrition of children under five years old.


Author(s):  
Salim Alanazy

The current study aims to develop smart learning environments in Saudi universities in line with the future requirements of artificial intelligence. To achieve this goal, a systematic review was conducted on studies published on Scopus and Google Scholar databases from 1990 until 2021 on the development of e-learning in the light of artificial intelligence (in addition to the relevant Arab studies). First, a list of challenges and opportunities for developing smart learning environments according to the future requirements of artificial intelligence was composed. Then, a questionnaire was prepared and reviewed by several academic experts in educational technology in Saudi universities. The study results include many challenges expected to be encountered in the smart learning environments in Saudi universities concerning the future preconditions for artificial intelligence. It also presented a number of opportunities and procedures for facing such challenges and exploiting the opportunities. Finally, some recommendations and suggestions were presented.


2013 ◽  
Vol 5 (2) ◽  
pp. 129-136 ◽  
Author(s):  
JA Syeda ◽  
M Nasser

An attempt was made to depict the valuable experience of farmers about climate change, environment and agricultural production, particularly wheat by conducting an opinion survey among 50 years and above aged farmers and agricultural workers in selected mauzas of Dinajpur district. Three hundred thirteen (313) respondents were interviewed in the survey. All the respondents opined regarding climate change in Dinajpur district over time. All of them opined that crop land, crop cultivation and crop yield were affected due to climatic change and changing of climate might pose a big and devastating threat to the production of wheat. Besides, the three case studies were accomplished to explore new ideas about climate change and the behavior of nature and human culture. They had also similar types of experience about climate change.DOI: http://dx.doi.org/10.3329/jesnr.v5i2.14804 J. Environ. Sci. & Natural Resources, 5(2): 129-136 2012


2021 ◽  
Vol 11 (24) ◽  
pp. 11820
Author(s):  
Paweena Suebsombut ◽  
Aicha Sekhari ◽  
Pradorn Sureephong ◽  
Abdelhak Belhi ◽  
Abdelaziz Bouras

Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.


2018 ◽  
pp. 47-58
Author(s):  
Miklós Neményi

According to Kay et al. (2004, in Shockley et al., 2017), there are seven steps to the decision-making process: 1) Identify the problem or opportunity, 2) Identify the alternative solution, 3) Collect all data and information, 4) Analyse the alternatives and make a decision, 5) Implement the decision, 6) Monitor the results of the decision, 7) Accept responsibility for the decision. The basic question is what kind of tasks we can perform in the decision-making process and what to leave for Artificial Intelligence (AI).


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Rasmus Einarsson ◽  
Alberto Sanz-Cobena ◽  
Eduardo Aguilera ◽  
Gilles Billen ◽  
Josette Garnier ◽  
...  

AbstractThis paper presents EuropeAgriDB v1.0, a dataset of crop production and nitrogen (N) flows in European cropland 1961–2019. The dataset covers 26 present-day countries, detailing the cropland N harvests in 17 crop categories as well as cropland N inputs in synthetic fertilizers, manure, symbiotic fixation, and atmospheric deposition. The study builds on established methods but goes beyond previous research by combining data from FAOSTAT, Eurostat, and a range of national data sources. The result is a detailed, complete, and consistent dataset, intended as a basis for further analyses of past and present agricultural production patterns, as well as construction of scenarios for the future.


Author(s):  
M.G. Shulskyi

The article deals with the study of the development of crop production in the farms of Lviv region. At the same time, considerable attention is focused on the important components of the functioning of any socio-economic phenomenon: past, present, future. Within this famous triad, we will analyze the activity of Lviv farmers during 2005–2016, because it is the time factor that determines the processes of functioning of any forms of management in agricultural production, an important part of which is farming. As a result of the research, it was established that the number of farms in the Lviv region is decreasing, but land is increasing, that is, there are processes of concentrating agricultural production in some farms and reducing them in others. These changes are objective and regular in nature and will take place in the future. Of particular interest are agricultural production data for farms in Lviv Oblast, whose volumes have a strong upward trend over the period under investigation. However, when analyzing the output figures in terms of the main branches of agriculture, there are some discontinuation processes – crop production is increasing, and animal husbandry – on the contrary, starting from 2010, they tend to decrease. During the research period there were some changes in the indicators of the structure of manufactured products in the sectoral section. So, let's say, when in 2005 crop production accounted for 63.8% of the total output of farm products, then in 2010 this figure dropped to 33.2%. In subsequent years there was an increase in the share of crop production, but this increase did not achieve the achieved level of 2005. The detailed analysis of the indices of production of agricultural crops by farmers by farmers, their yields, volumes of sales of crop production and their effectiveness give grounds to assert that positive developments have been made in this area. However, on the other hand, although farms have achieved some success in their activities, but the available resources are not fully used to improve the efficiency of farm households. These problems, both in modern conditions and for the future, should be solved in the complex: increasing the efficiency of the activity of farmers, on the one hand, and providing them with effective state support, on the other.


Author(s):  
Mironenko, V.

Purpose. Formalize requirements to information systems “smart” machines to increase efficiency of agro industrial production. Methods. Analysis of the possibilities of improving the efficiency of agricultural production by building a hardware based control systems with elements of artificial intelligence. Synthesis of the systems of automatic control by technological processes of crop production on the basis of modern information technologies. Results. Components of the information “intelligent” machines in the plant. The core technology of information mining. Conclusions. Further development of the maintenance of agricultural production should be based on creating technology 5th technological level, which involves the saturation technique by means of information, computing and electronics for operational changes in modes of working bodies in order to achieve the optimum phase condition of the object being processed. Keywords: operations systems of adaptive management, artificial intelligence, information technology.


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