WEATHER BASED CROP SELECTION AND PROCESSING FOR SMART AGRICULTURE USING MACHINE LEARNING TECHNIQUES

Authors

  • Dr.Kedri Janardhana, Dr.Shaik Khaleeel Ahamed , D.Sathish Kumar

Abstract

Modern Agriculture assumes an essential part in Indian economy. Yield expectation is a vital issue in rural. Any rancher is keen on knowing how much yield he is going to anticipate. Previously, yield expectation was performed by thinking about rancher's experience on specific field and harvest. The yield expectation is a significant issue that stays to be addressed dependent on accessible information. Information mining methods are the better decision for this reason. Diverse Data Mining methods are utilized and assessed in horticulture for assessing what's to come year's yield creation. This examination proposes and actualizes a framework to anticipate crop yield from past information. This is accomplished by applying affiliation rule mining on farming information. This examination centers around production of an expectation model which might be utilized to future forecast of harvest yield. This paper presents a short examination of harvest yield expectation utilizing information mining method dependent on affiliation rules for the chose district for example locale of Tamil Nadu in India. The test results shows that the proposed work proficiently anticipate the harvest yield creation.

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Published

2021-03-01

How to Cite

Dr.Kedri Janardhana, Dr.Shaik Khaleeel Ahamed , D.Sathish Kumar. (2021). WEATHER BASED CROP SELECTION AND PROCESSING FOR SMART AGRICULTURE USING MACHINE LEARNING TECHNIQUES. International Journal of Modern Agriculture, 10(1), 462-468. Retrieved from http://www.modern-journals.com/index.php/ijma/article/view/603

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Articles