E. Dinesh ¹, L. Ramesh ¹

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur – 639113, Tamil Nadu, IN

Abstract

Objectives: To monitor the agriculture environment parameters through IoT, cloud computing and Big Data. Methods/Statistical analysis: The proposed work which influences utilization of different advances to like Enormous Information, Internet of Things (IoT), Cloud computing, and so on will be a major shelter to the rancher who generally is made to experience an extreme time in perspective of absence of the innovation that he/she ought to have been adjusted at this point. Findings: The proposed one will give various administrations to the agriculturists that incorporate yield administration, advertising, back administration, online business, Web benefits through cloud and so on which likewise will decrease the joblessness issue in the adolescent. It additionally makes farming a calling for living as well as a productive area in the globe which additionally improves the Gross domestic product. Application/Improvements: In the event that this framework actualized by the ranchers, at that point the economy of the general population and nation will be made strides.

Keywords

Agriculture, Big Data, Cloud Computing, Farming, Cloud Services, Internet of Things

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L. Ramesh ¹, E. Dinesh ¹

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, IN

Abstract

Objectives: To detect fire in forest areas using spatial information. Methods/Statistical Analysis: The spatial data help in finding the areas powerless against woodland fire with the guide of Image handling. The Fuzzy C Mean algorithm is employed in design of the effective system. Initially, the images with the presence of fires are converted from RGB to XYZ color space. These images are clustered using FCM algorithm which locates the regions of fire. Findings: At first, the pictures with the nearness of flames are changed over from RGB to XYZ shading space. Thus, the XYZ shading space estimations of pixels which are bunched for recognizing the nearness of flames. Subsequently our composed successful framework will help the general population in reconnaissance to recognize timberland fires and to take suitable activities. Application/Improvements: This system is designed and produces the effective result for the people in surveillance of fire areas.

Keywords

CIE, Fuzzy C Means Clustering, K Means Clustering, XYZ

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