期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：8
DOI：10.14569/IJACSA.2018.090844
出版社：Science and Information Society (SAI)
摘要：Agriculture is the backbone of Indian economy and is the main income source for most of the population in India. So farmers are always curious about yield prediction. Crop yield depends on various factors like soil, weather, rain, fertilizers and pesticides. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decision and policies which lead to increased production. The main drawbacks of Indian farmers are they do not have proper knowledge regarding crop yield based on soil necessities. So in this paper, we proposed and developed an Improved Hybrid Model (which is combination of both classification, i.e. Artificial Neural Networks and clustering approach i.e. k-means (works based on Euclidean distance)) to provide awareness, usage and prediction to each farmer that relates to classify different crop yield representation based on soil necessity. For that we collected farmer’s data from standard repositories like http://www.tropmet.res.in/static_ page.php?page_id=52#data and then using that data provide awareness and other parameter sequences to all the farmers in India. Our experimental results show efficient e-agriculture with respect to user awareness, usage and prediction with respect to prediction, recall and f-measure for supporting real time marketing of different agriculture products.
关键词：Agriculture products; e-agriculture; classification; clustering; ensemble model
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