An artificial intelligence analysis of natural conditions in agriculture

Research Article
Open access

An artificial intelligence analysis of natural conditions in agriculture

Yi Du 1*
  • 1 Boston University    
  • *corresponding author duyi233@bu.edu
TNS Vol.5
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-915371-53-9
ISBN (Online): 978-1-915371-54-6

Abstract

In this study, we developed a machine learning algorithm for crop recommendation based on a dataset containing environmental features and corresponding optimal crop choices. The algorithm was trained on a set of seven numerical features, including temperature, humidity, pH value, rainfall, and levels of nitrogen, phosphorus, and potassium in the soil. We evaluated the performance of multiple classification algorithms, including naive Bayes, logistic regression, support vector machine (SVM), decision trees, random forests, and neural networks. Our results showed that neural networks performed the best. However, SVM, which provided only unsatisfactory results initially, also achieved pleasing results after we redesigned its structure and adjusted its parameters. This system has the potential to assist farmers in choosing the most suitable crops for their specific environments, leading to increased crop yield and profitability.

Keywords:

crop recommendation, intelligent agriculture, supervised learning, support vector machine, neural network

Du,Y. (2023). An artificial intelligence analysis of natural conditions in agriculture. Theoretical and Natural Science,5,847-856.
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References

[1]. Jingzhu Zhao, Qishan Luo, Hongbing Deng, and Yan Yan. Opportunities and challenges of sustainable agricultural development in china. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1492):893-904, 2008.

[2]. URMIL VERMA et al. Arima and arimax models for sugarcane yield forecasting in northern agro-climatic zone of haryana. Journal of Agrometeorology, 24(2):200-202, 2022.

[3]. Geoffrey I Webb, Eamonn Keogh, and Risto Miikkulainen. Naïve bayes. Encyclopedia of machine learning, 15:713-714, 2010.

[4]. Maher Maalouf. Logistic regression in data analysis: an overview. International Journal of Data Analysis Techniques and Strategies, 3(3):281-299, 2011.

[5]. Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18-28, 1998.

[6]. Gérard Biau and Erwan Scornet. A random forest guided tour. Test, 25(2):197-227, 2016.

[7]. Shun-ichi Amari and Si Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6):783-dfes 789, 1999.

Cite this article

Du,Y. (2023). An artificial intelligence analysis of natural conditions in agriculture. Theoretical and Natural Science,5,847-856.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)

ISBN：978-1-915371-53-9(Print) / 978-1-915371-54-6(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.5
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Jingzhu Zhao, Qishan Luo, Hongbing Deng, and Yan Yan. Opportunities and challenges of sustainable agricultural development in china. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1492):893-904, 2008.

[2]. URMIL VERMA et al. Arima and arimax models for sugarcane yield forecasting in northern agro-climatic zone of haryana. Journal of Agrometeorology, 24(2):200-202, 2022.

[3]. Geoffrey I Webb, Eamonn Keogh, and Risto Miikkulainen. Naïve bayes. Encyclopedia of machine learning, 15:713-714, 2010.

[4]. Maher Maalouf. Logistic regression in data analysis: an overview. International Journal of Data Analysis Techniques and Strategies, 3(3):281-299, 2011.

[5]. Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. Support vector machines. IEEE Intelligent Systems and their applications, 13(4):18-28, 1998.

[6]. Gérard Biau and Erwan Scornet. A random forest guided tour. Test, 25(2):197-227, 2016.

[7]. Shun-ichi Amari and Si Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6):783-dfes 789, 1999.

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