Crop yield prediction and Fertilizer Recommendation using Voting Based Ensemble Classifier
Author(s):
K. PRAGATHI
Keywords:
Crop, SVM, KNN, Random Forest, Nitrogen, Phosphorous, CNN
Abstract
India being an agriculture country, its economy predominantly depends on agriculture yield growth and agroindustry products. Data Mining is an emerging research field in crop yield analysis. Yield prediction is a very important issue in agricultural. Any farmer is interested in knowing how much yield he is about to expect and what is the crop that is suitable for the land. Analyze the various related attributes like location, pH value from which alkalinity of the soil is determined. Along with it, percentage of nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K) Location is used along with the use of third-party applications like APIs for weather and temperature, type of soil, nutrient value of the soil in that region, amount of rainfall in the region, soil composition can be determined. All these attributes of data will be analyzed, train the data with various suitable machine learning algorithms like SVM, Random Forest, KNN and voting classifier for creating a model. The system comes with a model to be precise and accurate in predicting crop yield and deliver the end user with the proper recommendations about required fertilizer ratio based on atmospheric and soil parameters of the land which enhance to increase the crop yield and increase farmer revenue. Thus, the proposed system takes the data regarding the quality of soil and the weather-related information as an input. The quality of the soil such as Nitrogen, Phosphorous, Potassium and Ph value. Weather related information like Rainfall, Temperature and Humidity to predict the better crop. In our project we are taking the datasets from Kaggle website.
Article Details
Unique Paper ID: 153341

Publication Volume & Issue: Volume 8, Issue 6

Page(s): 510 - 516
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