An effective data mining techniques based optimal paddy yield cultivation: a rational approach

From ancient times, the economic growth of India is mainly based on agricultural output. Agriculture is demographically the broadest economical sector and acts as an important part in the entire socio-economic fabric of India. Nearly half of the population of our country are connected to agriculture...

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Bibliographic Details
Published in:Paddy and water environment 2021-07, Vol.19 (3), p.331-343
Main Authors: Vinoth, B., Elango, N. M.
Format: Article
Language:English
Subjects:
Agricultural biotechnology
Agricultural land
Agricultural production
Agricultural technology
Agriculture
Bayesian analysis
Biomedical and Life Sciences
Classifiers
Crop production
Crop yield
Data analysis
Data mining
Economic development
Economic growth
Economics
Ecotoxicology
Fertilizers
Geoecology/Natural Processes
Hydrogeology
Hydrology/Water Resources
Information technology
Life Sciences
Rain
Rainfall
Review
Socioeconomic aspects
Soil Science & Conservation
Soil types
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Summary:From ancient times, the economic growth of India is mainly based on agricultural output. Agriculture is demographically the broadest economical sector and acts as an important part in the entire socio-economic fabric of India. Nearly half of the population of our country are connected to agriculture as their industry and make a livelihood out of it. At the same time, the crop productivity depends upon based on several climatic and economical factors namely soil type, weather, irrigation, fertilizer, rainfall, and so on. The present advancements in Information Technology for agriculture field become a hot research topic for predicting the crop yield, which can be resolved by the use of data mining techniques. This paper focuses to design a new predictive approach which offers a high yield of paddy crops by the use of data mining models and Hungarian model in Kuruvai season which generally starts from June to July. The data set used in this research for mining process is real data of Kuruvai season are collected from Tamil Nadu Agricultural University (TNAU) AgriTech Portal, Aduthurai, Thanjavur district and a set of three data mining approaches namely Apriori method, Naive Bayes (NB) and J48 classifier are employed to predict the paddy yield. The outcome of various models has been analyzed, and it is shown that the NB model has offered superior outcome over the compared classifier models.
ISSN:1611-2490
1611-2504
DOI:10.1007/s10333-021-00845-8