Crop Yield Prediction-Reference-Cited by-同舟云学术

Crop Yield Prediction

Published:2021-06-15 Issue: Volume: Page:561-569
ISSN:2456-3307
Container-title:International Journal of Scientific Research in Computer Science, Engineering and Information Technology
language:en
Short-container-title:IJSRCSEIT

Author:

Mahore Pallavi Shankarrao¹,Bardekar Dr. Aashish A.¹

Affiliation:

1. Sipna College of Engineering and Technology, Amravati, Maharashtra, India

Abstract

Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance for crop yield is compared with Random forest, Support Vector Machine, Weather, K Nearest neighbor. , which shows better performance results for each selected weather parameters. Crop yield rate depends upon various parameters such as the geography of area, soil type, soil nutrients, soil alkaline, weather condition, etc. The combination of these parameters can be used for selection of suitable crops for a farm or land to gain maximum yield. In this manuscript, soil and weather parameters such as soil type, soil fertility, maximum temperature, minimum temperature, rainfall are used to identify suitable crops for specified farm or land.

Publisher

Technoscience Academy

Subject

General Medicine

Reference12 articles.

1. Mann, M. L., Warner, J. M., & Malik, A. S. (2019). Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia. Climatic Change, 154(1- 2), 211-227.

2. Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and electronics in agriculture, 151, 61-69.

3. Chipanshi, A., Zhang, Y., Kouadio, L., Newlands, N., Davidson, A., Hill, H., ... & Reichert, G. (2015). Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agricultural and Forest Meteorology, 206, 137-150.

4. Johnson, M. D., Hsieh, W. W., Cannon, A. J., Davidson, A., & Bédard, F. (2016). Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and forest meteorology, 218, 74-84.

5. Rao, K. R., & Josephine, B. M. (2018, October). Exploring the Impact of Optimal Clusters on Cluster Purity. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 754-757). IEEE.

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