A bootstrap aggregation approach for adequate crop fertilizer and nutrition recommendation

Varshitha D. N., Savita Choudhary


Agriculture is theĀ largest workforce of India and biggest contributor to the Indian economy. Improving agricultural practices with the help of modern computer science technologies have great scope. Helping the farmers to know about their soil fertility, crops which can be grown and fertilizers or nutrients required for their land will be valuable inputs for them. Too much or too little fertilizers may harm the soil, so right amount of fertilization is also important. In this paper we have discussed about the bootstrap aggregation regression method, which is an ensemble machine learning technique to recommend the optimum level of nutrients and fertilizers. Hence customized nutrients recommendation reports could be generated to suggest the fertilizers and nutrients with their adequate quantities. This will be really beneficial for farmers to maintain the soil health and helpful for better crop growth and yield. We consider the features and levels of soil parameters such as nitrogen, phosphorus, potassium (NPK), pH level, organic carbon, electric conductivity, humidity, rainfall and other micro nutrients for predicting the right amount of fertilizers and nutrients. We have also checked other regression methods to compare the results based on the previous work done in the same field.


Bootstrap aggregation; Fertilizers; Machine learning; Nutrients; Regression; Soil parameters;

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DOI: http://doi.org/10.11591/ijeecs.v26.i3.pp1773-1780


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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