Abstract
Introduction: Identification of important soil nutrients is a very important task for precision farming and developing efficient machine learning models.
Method: 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.
Result: 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.
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