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Title: :  AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION
PaperId: :  17450
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 3 2022
DUI:    16.0415/IJARIIE-17450
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
PAVITHRA S RANAND INSTITUTE OF HIGHER TECHNOLOGY
PRIYA DARSHINI GANAND INSTITUTE OF HIGHER TECHNOLOGY
MAHESHWARI M ANAND INSTITUTE OF HIGHER TECHNOLOGY
MALATHI AANAND INSTITUTE OF HIGHER TECHNOLOGY

Abstract

Computer Science and Engineering
Precision Agriculture, Feature Scaling, Random Forest algorithm, Adaboost Classifier, Gradient Boosting Classifier, Crop yield, and K-Nearest Neighbor.
Agriculture has a significant part in the Indian economy and jobs. The most common problem faced by Indian farmers is that they do not select crops based on soil requirements, resulting in major productivity concerns. Machine learning algorithms will assist farmers in determining which crop to plant in order to provide the most yield by taking into account parameters such as temperature, rainfall, location, and so on. Precision agriculture can help overcome this challenge. This method considers three elements: soil characteristics, soil kinds, and crop yield data gathering. Based on these parameters, the farmer is recommended a crop to produce. This system includes a model for estimating crop yield that is exact and accurate. This system includes a model that is exact and accurate in predicting crop production and providing the end-user with the appropriate fertilizer ratio recommendations based on atmospheric and soil factors of the land, hence increasing crop yield and farmer revenue. As a result, the suggested system's pre-processing methods are based on feature scaling techniques including Min-Max Scaling, Standardization, and Normalization, which help to extract data on soil quality and weather-related information as an input. The soil's quality, such as nitrogen, phosphorus, potassium, pH, Rainfall, temperature, and humidity are all weather-related variables that can be used to predict a better harvest. We are using datasets from the Kaggle platform for our project. Using the ensemble modeling approach, we were able to do this. Different machine learning techniques, such as the Random Forest algorithm, Adaboost Classifier, Gradient Boosting Classifier, and K-Nearest Neighbors, were compared.

Citations

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IJARIIE PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. "AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION" International Journal Of Advance Research And Innovative Ideas In Education Volume 8 Issue 3 2022 Page 4647-4656
MLA PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. "AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION." International Journal Of Advance Research And Innovative Ideas In Education 8.3(2022) : 4647-4656.
APA PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , & MALATHI A. (2022). AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION. International Journal Of Advance Research And Innovative Ideas In Education, 8(3), 4647-4656.
Chicago PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. "AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION." International Journal Of Advance Research And Innovative Ideas In Education 8, no. 3 (2022) : 4647-4656.
Oxford PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. 'AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION', International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, 2022, p. 4647-4656. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/AN_ENSEMBLE_ALGORITHM_FOR_CROP_YIELD_PREDICTION_ijariie17450.pdf (Accessed : ).
Harvard PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. (2022) 'AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION', International Journal Of Advance Research And Innovative Ideas In Education, 8(3), pp. 4647-4656IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/AN_ENSEMBLE_ALGORITHM_FOR_CROP_YIELD_PREDICTION_ijariie17450.pdf (Accessed : )
IEEE PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A, "AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION," International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, pp. 4647-4656, May-Jun 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/AN_ENSEMBLE_ALGORITHM_FOR_CROP_YIELD_PREDICTION_ijariie17450.pdf [Accessed : ].
Turabian PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. "AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 8 number 3 ().
Vancouver PAVITHRA S R, PRIYA DARSHINI G, MAHESHWARI M , and MALATHI A. AN ENSEMBLE ALGORITHM FOR CROP YIELD PREDICTION. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2022 [Cited : ]; 8(3) : 4647-4656. Available from: https://ijariie.com/AdminUploadPdf/AN_ENSEMBLE_ALGORITHM_FOR_CROP_YIELD_PREDICTION_ijariie17450.pdf
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