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India being an agriculture country, its economy predominantly depends on agriculture yield growth and agroindustry products. Yield forecasting is a very important issue in agriculture. In order to predict the crop yield, all the attributes of data will be analyzed like temperature, type of soil, Nutrient value of the soil in that region, amount of rainfall in that region, soil composition can be determined. We train the data with various suitable machine learning algorithms like random forest and voting classifier for creating a model. The system comes with a model to be precise and accurate in forecasting crop yield and deliver 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. Weather related information like rainfall, temperature and humidity used to forecast the better crop.
Keywords:
Yield Forecasting, Random Forest, Voting Classifier, Fertilizer Recommendation
Cite Article:
"Crop yield forecasting and Fertilizer recommendation using Voting classifier", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.1072 - 1074, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306158.pdf
Downloads:
000205540
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator