Image recognition in the artificial agriculture officer

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Farmers face a multitude of problems nowadays such as lower crop production, tumultuous weather patterns, and crop infections. All of these issues can be solved if they have access to the right information. The current methods of information retrieval, such as search engine lookup and talking to an Agriculture Officer, have multiple defects. A more suitable solution, that we are proposing, is an android application, available at all times, that can give succinct answers to any question a farmer may pose. The application will include an image recognition component that will be able to recognize a variety of crop diseases in the case that the farmer does not know what he is dealing with and is unable to describe it.  Image recognition is the ability of a computer to recognize and distinguish between different objects, and is actually a much harder problem to solve than it seems. We are using Tensorflow, a tool that uses convolutional neural networks, to implement it

     

     

  • Keywords


    Android application; Artificial agriculture officer; Convolutional Neural Networks; Image Recognition; Tensor flow.

  • References


      [1] Sharwari Gaikwad, Rohan Asodekar, Sunny Gadia, and Vahida Z. Attar, “AGRI-QAS question-answering system for agriculture domain,” in Advances in Computing, Communications and Informatics, 2015 International Conference.

      [2] Pooja Kamavisdar1 , Sonam Saluja2 , Sonu Agrawal3“ A Survey on Image Classification Approaches and Techniques“ International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 1, January 2013

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      [4] FatihErtam, GalipAydin, “Data classification with deep learning using Tensorflow” International Conference on Computer Science and Engineering (UBMK), October 2017

      [5] JaspreetKaur and Vishal Gupta, “Effective Question Answering Techniques and their Evaluation Metrics”, International Journal of Computer Applications (0975 – 8887) Vol 65– No.12, March 2013

      [6] .JianxinWu,”Efficient HIK SVM Learning for Image Classification”, IEEE Transactions on Image Processing, Vol. 21, No. 10, October 2012

      [7] XiaohongYu, and Hong Liu Huangshan, “Image Semantic Classification Using SVM in Image Retrieval” P. R. China, 26-28, December 2009

      [8] Y. Koren, “Collaborative filtering with temporal dynamics,” in Proc.KDD, pp. 447–456.Paris, France, 2009


 

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Article ID: 14500
 
DOI: 10.14419/ijet.v7i3.3.14500




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