Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification

  • Gnanasaravanan S Department of Electronics and communication engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
  • Tharani B Department of Electronics and communication engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
  • Mona Sahu Department of Mechanical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.
Keywords: Recurrent Neural Networks, Long Short-Term Memory, Feature Extraction, Classification

Abstract

Farming profitability is something on which economy profoundly depends. This is the one reason that sickness recognition in plants assumes a critical job in farming field, as having infection in plants are very common. In the event that legitimate consideration isn't taken here, it causes genuine consequences for plants and because of which particular item quality, amount or profitability is influenced. This paper displays an algorithm for image segmentation technique which is utilized for automatic identification and classification plant leaf infections. It additionally covers review on various classification techniques that can be utilized for plant leaf ailment discovery. As the infected regions vary in length it is difficult to develop a feature vector of identical finite length representing all the sequences. A simple method to go around this issue is given by Recurrent Neural Networks (RNN). In this work we separate a feature vector through the use of Long Short-Term Memory (LSTM) recurrent neural networks. The LSTM network recursively repeats and concentrates two limited vectors whose link yields finite length vector portrayal.

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Published
2021-10-30
How to Cite
S, G., B, T., & Sahu, M. (2021). Long Short-Term Memory Recurrent Neural Networks for Plant disease Identification. International Journal of Computer Communication and Informatics, 3(2), 51-62. https://doi.org/10.34256/ijcci2125



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