CRY &amp;#x2014; An improved crop yield prediction model using bee hive clustering approach for agricultural data sets

Ananthara, M. G.; Arunkumar, T.; Hemavathy, R

doi:10.1109/icprime.2013.6496717

Cited by 21 publications

(1 citation statement)

References 13 publications

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“…If unsuitable location is opted for the growth of plant, then there will be failure in crop yield. The outcome of crop yield primarily depends on parameters such as variety of crop, seed type, and environmental parameters such as sunlight (Temperature), soil (ph), water (ph), rainfall, and humidity …”

Section: Introductionmentioning

confidence: 99%

Analysis of crop suitability using clustering technique in Coimbatore region of Tamil Nadu

S¹,

Babu²

2019

Concurrency and Computation

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Summary Agricultural production is a important thing that should be maintained because need of crop is increasing for the current population. They are affected by various factors of environmental conditions, temperature, soil conditions, etc. Most of the farmers cultivate crops without the knowledge about crop suitability in their location there by reducing the crop productivity and money loss. It tends to cause the economical level of country. Therefore, it is necessary to develop a method to predict the crop productivity. In this work, Coimbatore region is taken for case study to analyze the crop productivity. For this process, new data mining technique is proposed and employed to predict the crop suitability. The proposed method produces the better results in terms of prediction level in region and sub regions of given area.

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Section: Introductionmentioning

confidence: 99%