Abstract
Agriculture is the main occupation in Andhra Pradesh (A.P.), the atmosphere and land of A.P. is appropriate for cultivating variety of crops like rice, wheat and cotton. Crop protection is one of the foremost challenges in agriculture. Pest classification during unusual weather conditions is very strong confront and the goal of every farmer is to protect the crops in exact time. Big Data analytics is playing a dominant role in agriculture sectors which helps in making good decisions to prevent any crops loss. Machine learning algorithm is a best analytical platform that automates analytical model building. Classification is a main method of machine learning which is useful to classify the problem and provide better solutions. This paper, demonstrates the implementation of three classifiers K-Nearest Neighbor (K-NN), Naive Bayes (NB), Decision Tree (DT) on cotton pests data, out of which the decision tree classifier proved to be the best for analysis.
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Durgabai, R.P.L., Bhargavi, P., Jyothi, S. (2020). Classification of Cotton Crop Pests Using Big Data Analytics. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_4
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DOI: https://doi.org/10.1007/978-3-030-46939-9_4
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