Abstract
Agriculture sector is recognized as the backbone of the Indian economy that plays a crucial role in the growth of the nation’s economy. It imparts on weather and other environmental aspects. Some of the factors on which agriculture is reliant are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides, and herb. The soil fertility is dependent on these factors and hence difficult to predict. However, the Agriculture sector in India is facing the severe problem of increasing crop productivity. Farmers lack the essential knowledge of nutrient content of the soil, selection of crop best suited for the soil and they also lack efficient methods for predicting crop well in advance so that appropriate methods have been used to improve crop productivity. This paper presents different Supervised Machine Learning Algorithms such as Decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) to predict the fertility of soil based on macro-nutrients and micro-nutrients status found in the dataset. Supervised Machine Learning algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using R Tool. The performance analysis of these algorithms is done using different evaluation metrics like mean absolute error, cross-validation, and accuracy. Result analysis shows that the Decision tree is produced the best accuracy of 99% with a very less mean square error (MSE) rate.
Highlights
Data mining could be a fairly immature and interdisciplinary sector of computer science, is that the process that attempts to mining patterns in large data sets
Three Machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree have been applied to a trained dataset
The data set consists of a variety of parameters that are useful for identifying the status of fertility and conducting supervisory training on data sets collected from the agriculture domain to divide information into multiple classes
Summary
Data mining could be a fairly immature and interdisciplinary sector of computer science, is that the process that attempts to mining patterns in large data sets. It utilizes methods at the connection of statistics, artificial intelligence, machine learning, and database systems. The data mining task aims to extract information from a knowledge set and transform it into a comprehensible form for further use. Predictive analysis is that the task of gathering information from soil datasets to seek out future outcomes. Soil fertility is carried out which involves predicting the yield of the crop based on the existing data. Data mining techniques are useful for predicting the fertility of the soil. Soil fertility depends on various factors [3] and depends on: Prediction models are essentially two main categories
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