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.

Keywords: Soil Health Analysis, feature engineering, multiple agri-advices, chi-square, ANOVA, mutual information gain, scoring functions.