Fuzzy Logic Approach for Identifying the Effects of Climate Change on Agricultural Production

Shahjalal, M.; Alam, Md. Zahidul; Miah, Saikh Shahjahan; Chowdhury, Abdul Hannan

doi:10.11648/j.ijae.20210604.15

Cited by 4 publications

(2 citation statements)

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“…Agricultural production is affected by climate change and natural disasters, resulting in significant economic losses for farmers. In order to assist farmers in making decisions regarding the selection of the right crops, a set of fuzzy rules was employed, incorporating a combination of climate variables such as temperature, weather disasters, water availability, pest levels, diseases, species extinction, and deforestation, to estimate the rate of agricultural production [16].…”

Section: Crop Management and Yield Optimizationmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

Bezas,

Foteini Filippidou

2023

ijcs

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In recent years, the agricultural sector has been undergoing a new "green revolution" characterized by the increasing use of information and communication technology (ICT) and the transition from traditional farming methods to smart agricultural practices, also known as Agriculture 4.0. Robotics, combined with the use of drones, the emerging field of the Internet of Things (IoT), machine learning, and artificial intelligence, are now being deployed in digital transformation services in agriculture, aiming to optimize crop performance and agricultural sustainability. According to research and international literature, the new trends are now oriented towards the development of global and state-of-the-art connected agricultural systems through digital management platforms, with the goal of facilitating the flow of data and information. Although many efforts are being made to implement smart agriculture, there are still challenges that require further research.

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Section: Crop Management and Yield Optimizationmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

Bezas,

Foteini Filippidou

2023

ijcs

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“…Using fuzzy logic, Shahjala investigated the effects of climate change and natural catastrophes on agricultural harvest in Bangladesh, which results in massive losses for farmers [21]. According to the research, as temperatures and weather disasters rise to their peak levels, the harvest falls to its lowest point.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Climate Effect on Arable Crops Production of Oryza Sativa and Zea Mays in Nigeria Using Fuzzy Logic

Aimufua

Akimoh

Jegede

2022

AJEAT

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In Nigeria, farmers account for more than a third of the labor force. Despite the government's and other relevant stakeholders' strategies and measures to address climate change's impact on agricultural activities in Nigeria, the country has continued to be threatened by climate change, which has had negative consequences for food production, particularly rice and maize, which are the country's main staple foods. High precipitation owing to moisture levels negates agricultural yields, according to the impacts of temperature and moisture levels on agriculture productivity. Weather disasters, deforestation, disease transmission, and species extinction are all climate variables that limit agricultural production rates. The goal of this research is to use machine learning algorithms to determine the best weather conditions for arable crops. To model and predict agricultural yields, fuzzy logic was used. Secondary data, pre-processing and processing, modeling, and the user interface are the five sections that make up the system design. The harvesting outcomes for each of the months specified From January through December, the majority of the data obtained revealed Harvest values of low (54.8%), fair (23.6%), and good (21.5%) quality. Agricultural activities should be carried out between MAY and SEP, whereas crops that require a lot of rain should be sown between JUL and AUG when rainfall is at its highest. The study concluded that agricultural operations should be carried out between MAY and SEP in order to achieve good harvest quality, based on the data collected, extracted, and assessed. The findings of the study will guide policymakers and farmers toward a bumper harvest, increased income, and a rise in the country's GDP. Finally, crops that require a lot of rain, like rice, should be planted between July and August, according to the research.

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An intensified linear diophantine fuzzy combined DEMATEL framework for the assessment of climate crisis

Kannan,

Jayakumar,

Pethaperumal

et al. 2024

Stoch Environ Res Risk Assess

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Fuzzy Logic Approach for Identifying the Effects of Climate Change on Agricultural Production

Cited by 4 publications

References 0 publications

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture

Modeling of Climate Effect on Arable Crops Production of Oryza Sativa and Zea Mays in Nigeria Using Fuzzy Logic

An intensified linear diophantine fuzzy combined DEMATEL framework for the assessment of climate crisis

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