Papers

Title: Crop prediction and cultivation through climate smart agriculture
Year of Publication: 2020
Publisher: International Journal of Computer Systems (IJCS)
ISSN: 2394-1065
Series: Volume 07, Number 2, May 2020
Authors: B.S. Eeshitha Prasuna, Koncha da Saikrishna, Swarnalatha P

Citation:

B.S. Eeshitha Prasuna, Koncha da Saikrishna, Swarnalatha P , "Crop prediction and cultivation through climate smart agriculture", In International Journal of Computer Systems (IJCS), pp: 9-13, Volume 7, Issue 2, May, 2020. BibTeX

@article{key:article,
	author = {B.S. Eeshitha Prasuna, Koncha da Saikrishna, Swarnalatha P },
	title = {Crop prediction and cultivation through climate smart agriculture},
	journal = {International Journal of Computer Systems (IJCS)},
	year = {2020},
	volume = {7},
	number = {2},
	pages = {9-13},
	month = {May}
	}


Abstract

Achieving high crop yield is one of the major goals for farmers. Detecting the crop to be cultivated for suitable weather conditions is helpful for farmers and management of problems using crop yield indicators can increase the yield and profit. predictions are useful for crop owners to minimize the losses when conditions are not favorable and also to maximize the yield when conditions are favorable. Prediction of major crops like Rice, wheat, sugarcane, corn in India is helpful for the country's economy. These crops are dependent on climatic conditions so using the below mentioned systems we can predict them accurately. There are a lot of models and algorithms like statistical models, simulation models but using the Deep or machine learning models like SVM, Random Forrest classifier, and MLP classifier we can achieve the most accurate results. Agriculture is an important sector in India. It is the major source of economy. So, the researchers, scientists are trying their best to predict crop in a better way and to maximize profits in agricultural industry. Data mining and deep learning are two important fields in predicting crop yielding capacities. It uses recorded information such as data sets for training the system using deep learning algorithms like logistic regression, SVM and random forest classifier and others to achieve the best possible result.

References

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Keywords

Random forest classifier, machine learning, Deep learning, multi-layer perceptron classifier, decision tree, neural networks, logistic regression.