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Volume 19, Issue 6
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Introduction

Identification of important soil nutrients is a very important task for precision farming and developing efficient machine learning models.

Methods

The existing work shows that the patent is filed and published on a method and device for assessment of soil health parameters and recommendation of fertilizers. The existing work is done for one advice at a time not for several advices. Multiple advices that are taken into account for the task are appropriate crops, organic fertilizer, and combination 1 and combination 2 of fertilizers.

Results

This paper presented results of feature selection techniques based on Chi-Square, ANOVA and Mutual Information Gain scoring functions such as Select K Best and Select Percentile for multiple agri-advice datasets of Pune District regions to identify important soil health features.

Conclusion

As per Chi-Square, ANOVA and Mutual Information scoring functions with Select K Best and Select Percentile techniques ‘Mn’ was the most important parameter and Cu’ and ‘B’ were the least important parameters among all 11 parameters common in 4 agriculture advices. Whereas pH, K, Fe, 'Oc', 'N', 'S', 'Mn', and 'P' will be used for future research work on the development of an efficient classification algorithm for multi-advice generators.

© 2025 Bentham Science Publishers
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2024-05-09
2025-06-23

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/content/journals/eng/10.2174/0118722121290793240429111623
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Analysis of Soil Health Parameters to Identify Important Soil Nutrients and Weights using Feature Engineering for Multiple Agri-Advices
Recent Pat Eng 19, E090524229802 (2025); https://doi.org/10.2174/0118722121290793240429111623
/content/journals/eng/10.2174/0118722121290793240429111623
/content/journals/eng/10.2174/0118722121290793240429111623
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  • Article Type:     Research Article
Keyword(s): ANOVA; chi-square; feature engineering; multiple agri-advices; mutual information gain; scoring functions; Soil Health Analysis

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