Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production

Elsoragaby, Suha; Yahya, Azmi; Mahadi, Muhammad Razif; Nawi, Nolila Mohd; Mairghany, Modather; Elhassan, Sami Mustafa M; Kheiralla, A. F.

doi:10.1016/j.egyr.2020.10.010

Cited by 24 publications

(7 citation statements)

References 30 publications

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“…For the statistical analysis, all data of field performance, energy inputs, and GHG emissions were entered into MS Excel 2016 spreadsheet software and analyzed. A comparison between the main season and off-season and a comparison between tillage passes were conducted by using t-tests at a 95% confidence interval, assuming unequal variances [29,30].…”

Section: Methodsmentioning

confidence: 99%

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Energy Utilization and Greenhouse Gas (GHG) Emissions of Tillage Operation in Wetland Rice Cultivation

Elsoragaby,

Kheiralla,

Tola

et al. 2024

Land

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In Malaysia, wetland rice is cultivated over two cropping seasons: the main season, from June to November, and the off-season, from January to June. The aim of this study was to investigate tillage operations in rice production in relation to actual field operations and under real field conditions for two rice cultivation seasons. The results showed that 80.7%, 17%, and 2.3% of the total time was spent on the actual operation, turning time, and reversing time, respectively. The results also showed that the mean effective field capacity, field efficiency, and fuel consumption were 1.2 ha/h, 80%, and 7.6 L/ha, respectively. The distribution of energy used in the first, second, and third tillage passes amounted to 37%, 33%, and 30% of the total energy, respectively. Fuel, machinery, and total GHG emissions were 62.4, 7.6, and 70 kg CO2eq/ha, respectively. Fuel represented the highest contributor of energy expenditure and GHG emissions. The distributions of GHG emissions in the first, second, and third tillage passes were 37%, 32%, and 31% of the total GHG emissions. The results reveal that carrying out minimum-tillage operations led to a reduction in environmental impacts.

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Section: Methodsmentioning

confidence: 99%

“…parison between the main season and off-season and a comparison between tillage passes were conducted by using t-tests at a 95% confidence interval, assuming unequal variances [29,30].…”

Section: Field Performance Of Tractor In Tillage Operationmentioning

confidence: 99%

Energy Utilization and Greenhouse Gas (GHG) Emissions of Tillage Operation in Wetland Rice Cultivation

Elsoragaby,

Kheiralla,

Tola

et al. 2024

Land

Self Cite

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“…NN was then implemented on the selected attributes and financial crisis is easily identified. Elsoragaby et al [13] use Multi Objective Genetic Algorithm to measure the energy resources consumed by the rice crop in wetland cultivation. The authors conducted real time study in 62 farms and identified the various factors to minimize the emission of greenhouse gases.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Muthukumaran¹,

Geetha²,

Ramaraj³

2023

Intelligent Automation &Amp; Soft Computing

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Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy. There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind, relative humidity, Instant Wind Speed in paddy cultivation. The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series. A Robust Optimized Artificial Neural Network (ROANN) Algorithm with Genetic Algorithm (GA) and Multi Objective Particle Swarm Optimization Algorithm (MOPSO) proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation. A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database. The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database. Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm. The reason for improving the growth of paddy was identified using the output of the Neural Network. Performance metrics such as Accuracy, Error Rate etc were used to measure the performance of the proposed algorithm. Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

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“…In the process of genetic algorithm implementation, in order to maintain the diversity of individuals in the population, it is usually necessary to scale the fitness of individuals in the later stage of evolution to avoid premature convergence [20,21].…”

Section: Evaluation Of Ecological Agriculture Industrymentioning

confidence: 99%

Development and Comprehensive Evaluation of Ecological Agriculture Industry Chain System Based on Genetic Algorithm

Jia¹,

Chu²,

Niu³

et al. 2022

Advances in Multimedia

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Due to the constraints of traditional agricultural development factors, the existing agricultural industrial chain structure was unreasonable, which can not only promote the development of related industries but also affect the sustainable development of the whole agriculture. Therefore, it was of great significance to build a scientific and orderly ecological agriculture industry chain system and optimize it on the existing basis for improving the industrial benefits of modern agriculture and enhancing the vitality of agricultural development. From the perspective of the construction of an ecological agriculture industry chain system, this study put forward the development and evaluation system of the agricultural industry chain system based on genetic algorithm, in order to provide support for better promoting the development of modern agriculture. First, based on summarizing the related concepts of ecological agriculture and industrial structure, the definition and organization of agricultural industrial chain were given. Second, it expounded the basic theory of genetic algorithm and its application in the optimization of agricultural industrial structure, and put forward the optimization model of ecological agricultural industrial structure combined with the objectives of agricultural industry development. Finally, by constructing the evaluation index system of ecological agriculture industry chain system, a comprehensive evaluation model of ecological agriculture industry chain system based on genetic algorithm was designed. Through the case verification and comparative analysis, the results showed that the ecological agriculture industry chain system evaluation system and evaluation model based on genetic algorithm proposed in this study had certain feasibility and effectiveness, and can better evaluate the changing trend of various industries in the process of agricultural industry development.

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Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production

Cited by 24 publications

References 30 publications

Energy Utilization and Greenhouse Gas (GHG) Emissions of Tillage Operation in Wetland Rice Cultivation

Energy Utilization and Greenhouse Gas (GHG) Emissions of Tillage Operation in Wetland Rice Cultivation

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Development and Comprehensive Evaluation of Ecological Agriculture Industry Chain System Based on Genetic Algorithm

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