A Multigene Genetic Programming Approach for Soil Classification and Crop Recommendation
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- @InProceedings{khan:2023:ICICTD,
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author = "Ishrat Khan and Pintu Chandra Shill",
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title = "A Multigene Genetic Programming Approach for Soil
Classification and Crop Recommendation",
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booktitle = "Proceedings of International Conference on Information
and Communication Technology for Development",
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year = "2023",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, GPTIPS",
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URL = "http://link.springer.com/chapter/10.1007/978-981-19-7528-8_32",
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DOI = "doi:10.1007/978-981-19-7528-8_32",
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abstract = "The economy of Bangladesh depends to a large extent on
agriculture. Besides, a large number of the total
population are employed in this sector. In Bangladesh,
the population is fast expanding while the overall
amount of arable land is constantly diminishing.
Because various crops require different soil types,
identifying and selecting the proper kind of soil is
critical to ensuring optimal crop yield while working
with limited land resources. In this study, we present
a soil classification method using symbolic regression
of multigene genetic programming. Dataset for this work
is collected from Soil Resource Development Institute,
Government of the people republic of Bangladesh. GPTIPS
toolbox is used to select the appropriate features for
training and developing a mathematical model. In a
short period of time, the model generates correct
results for both the training and testing datasets.
Besides, the error rate for soil type classification is
extremely low. Finally, suitable crops are recommended
based on the accurate classification. According to the
results, the proposed multigene genetic programming
(MGGP)-based approach performs the best in terms of
accuracy, with an accuracy of 98.04 percent. Moreover,
our proposed soil classification method outperforms
many existing soil classification methods.",
- }
Genetic Programming entries for
Ishrat Khan
Pintu Chandra Shill
Citations