Application of fuzzy set theory in agro-meteorological models for yield estimation based on statistics

Sakharova, Lyudmila V.; Mikhail, Strukov; Imran, Akperov; Alekseychik, Tamara; Anatoliy, Chuvenkov

doi:10.1016/j.procs.2017.11.313

Cited by 12 publications

(5 citation statements)

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“…This, of course, requires a limitation on the number of classes, partitions, and possibilities for the response (output) of rule bases, naturally decreasing the accuracy of the model. These characteristics (and with 5 fuzzy sets) can be found in the field of agrarian sciences in Luydmila et al (2017) studying applications in agrometeorological models for productivity estimation, in Giusti & Marsili-Libelli (2015) creating a support system for irrigation and water conservation in agriculture, in Mamann et al (2018) in simulation of wheat production by nitrogen and hydrogel, and in Lima et al (2010) to control soil matrix potential.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of applications of different uses of this theory in agricultural engineering are characterized in models on cattle body mass (Gabriel Filho et al, 2011, dynamic quality of agricultural soils in relation to their biological, physical and chemistry characteristics (Rodríguez et al, 2016. ), development of irrigated lettuce with different types of water (Putti, 2015), commercialization of cassava (Gabriel Filho et al, 2015), estimate the effects of global warming on the vitality of Laelia purpurata orchids , crop classification (Murmu & Biswas 2015), land use planning of agricultural production systems (Mishra et al, 2014), agro-meteorological models for yield estimation (Luydmila et al, 2017) and agricultural optimal cropping pattern determination (Neamatollahi et al, 2017) Several models are developed using area experts for the development of rules base, system core based on fuzzy rules. However, many conducted agronomic experiments can, from their measured data, provide such information that field experts would do it, and with a degree of accuracy according to the data.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Modeling of the Effects of Irrigation and Water Salinity in Harvest Point of Tomato Crop. Part I: Description of the Method

et al. 2019

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It was used statistical techniques for the evaluation of agricultural experiments, but there are mathematical theories that allow finer adjustments, highlighting among them, the fuzzy logic. The objective of the study was characterizing a method of fuzzy modeling from an agronomic experiment. For this study it was used data from an experiment conducted at the School of Agriculture of São Paulo State University (UNESP) in Botucatu-SP. The system input variables based in fuzzy rules were soil water tension and doses of water salinity, being defined three fuzzy sets. The output variables was elected from the biometric and productivity analysis that showed statistically significant differences, namely, plant height, stem diameter, leaf area, green biomass, dry weight, number of fruits, average fruit weight and percentage of disabled fruits. For output variables 9 fuzzy sets were defined. From the adopted methodology, the model allowed extract directly from the data set a base of rules without the use of questionnaires to experts for its preparation. In addition, it will analyze intermediate regions at trial levels and weave other conclusions of the tomato growth and productivity, not limiting in this way only those observed with statistical analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling of the Effects of Irrigation and Water Salinity in Harvest Point of Tomato Crop. Part I: Description of the Method

et al. 2019

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“…In due course, McBratney and Odeh (1997) [43] described fuzzy systems, including fuzzy set theory and fuzzy logic, as a potential tool to significantly improve or extend conventional logic and demonstrate phenomena associated with soil through it. Another important work in meteorology is Luydmila et al (2017) [44], where the authors proposed a fuzzy-logic-based model for agro-meteorological modeling. The current approach differs from the earlier ones.…”

Section: Discussionmentioning

confidence: 99%

An Interpretation of the Surface Temperature Time Series through Fuzzy Measures

Devi

Chattopadhyay

2023

Axioms

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This paper reports a study to interpret the surface temperature based on time series and fuzzy measures. We demonstrated a method to identify the uncertainty around the surface temperature data concerning the summer monsoon in India. The random variables were standardized, and the Dempster-Shafer Theory was used to generate common goals. Two criteria, represented as fuzzy numbers, were used for this purpose. We constructed three polynomials to illustrate a functional connection between time series and the measure of joint belief. The analysis of the obtained results showed that the certainty increased over time. It confirmed that the degree of the evidence is a more predictable parameter at a more extended period.

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“…They considered the temperature, soil humidity and time duration to open the valves. Sakharova Luydmila et.al [ 10 ] proposed a fuzzy model for comprehensive evaluation towards sustainability of the crop culture. They utilized weight parameters during the evaluation process.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of Efficient Water Management for Agriculture using IoT and Fuzzy Logic

Satish*,

Raju,

Satish

et al. 2020

IJITEE

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Agriculture is a major source of living in India. The productive growth of the crop depends mainly on various sources such as adequate supply of water, fertilizers and soil conditions. Excessive water in the farming field may damage the crop and similarly limited supply of water results for causing diseases in crops. Therefore the proper management of water is required for better yield of the crops. In this paper, a system was designed and developed for efficient water management in agriculture field. This work utilized the advantages of Internet of Things (IoT) and Fuzzy logic. The proposed work reduces the burden on the farmers and increases GDP of Indian economy.

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Application of fuzzy set theory in agro-meteorological models for yield estimation based on statistics

Cited by 12 publications

References 0 publications

Fuzzy Modeling of the Effects of Irrigation and Water Salinity in Harvest Point of Tomato Crop. Part I: Description of the Method

Fuzzy Modeling of the Effects of Irrigation and Water Salinity in Harvest Point of Tomato Crop. Part I: Description of the Method

An Interpretation of the Surface Temperature Time Series through Fuzzy Measures

Design and Development of Efficient Water Management for Agriculture using IoT and Fuzzy Logic

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