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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Choudhary, Savita
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Electrical & Electronics Engineering

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The new machine learning feature selection method used in fertilizer recommendation D N, Varshitha; Choudhary, Savita
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7198

Abstract

Fertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health ,so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer nutrient recommender in our work from 85% to 98%. Feature selection raking has been introduced with random forest approach.
An artificial intelligence solution for crop recommendation N., Varshitha D.; Choudhary, Savita
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1688-1695

Abstract

Agriculture is the major occupation in India. The development of India is in the hands of farmers. Farmers are said to be our nation’s backbone, so there is a need to support our farmers technologically so that the difficulties of traditional agricultural practices would be overcome and also there will be positive impact on the yield, harvest, healthy crop output and the income of the farmers. Farmer needs awareness about his soil and the methods to improve his soil to grow the healthy crops. We propose an approach which involves deep learning and some IOT features to help our farmers. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it. We have developed a deep neural network model to predict the crop which can be suitably grown in the soil. We have also implemented the other machine learning classifiers on the same collected dataset to test the accuracies of each classifier and our deep neural network model.
Co-Authors D N, Varshitha N., Varshitha D.