Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier
|International Journal of Computer Science and Engineering|
|© 2020 by SSRG - IJCSE Journal|
|Volume 7 Issue 5|
|Year of Publication : 2020|
|Authors : K. Archana, Dr.K.G.Saranya|
How to Cite?
K. Archana, Dr.K.G.Saranya, "Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier," SSRG International Journal of Computer Science and Engineering , vol. 7, no. 5, pp. 1-4, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I5P101
Agriculture is the keystone of a developing country such as India. For the revenue, the majority of their population depends on agriculture. Machine Learning is an imminent field of informatics that can be applied quite efficiently to the agricultural sector. Crop yield prediction and forecasting is essential for agricultural stakeholders which can be acquired through machine learning techniques. When the farmers are not aware of the soil nutrition and soil composition that results in minimal crop yield. Thus the proposed system developed, which in turn focuses on the macronutrients (NPK), pH and electrical conductivity in the soil and temperature for providing the most appropriate crop suggestions. The proposed system constructs a collaborative system of crop rotation, crop yield prediction and forecasting and fertilizer recommendation. In this project a system is developed which incorporates the agricultural dataset wherein voting based ensemble classifier algorithm is applied to suggest the appropriate crops. Crop yield prediction and forecasting will increase the agricultural production. Periodical crop rotation will improve the soil fertility. This system supports farmer friendly fertilization decision making. The accuracy of this system was around 92%.
Nitrogen, Phosphorus, Potassium, soil nutrition, yield prediction, crop rotation, fertilize recommendation, Ensemble classifier, voting.
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