Crop Disease Detection using Machine Learning

Authors

  • Abi P Mathew Dept. of Electronics and Instrumentation Engineering, Federal Institute of Science and Technology, Kochi, Kerala, India
  • Sreehari Dept. of Electronics and Instrumentation Engineering, Federal Institute of Science and Technology, Kochi, Kerala, India.
  • P Viswajith Dept. of Electronics and Instrumentation Engineering, Federal Institute of Science and Technology, Kochi, Kerala, India.
  • Abdul Rahman Dept. of Electronics and Instrumentation Engineering, Federal Institute of Science and Technology, Kochi, Kerala, India.
  • Vishnu Murali Dept. of Electronics and Instrumentation Engineering, Federal Institute of Science and Technology, Kochi, Kerala, India.

DOI:

https://doi.org/10.61779/jasetm.v1i1.6

Keywords:

Node MCU, Arduino, Humidity Sensor, Moisture Sensor, CNN

Abstract

Agriculture is the primary source of food for the world's population, despite the rapid increase in population. Early detection of plant diseases in the field would be beneficial to improve crop production efficiency. Technology has become increasingly important in agriculture in recent years, as it is used to improve efficiency, reduce costs, and increase yields. The emergence of accurate techniques in the field of leaf-based image classification has shown impressive results. Our proposed work includes various phases of implementation of image classification, namely dataset creation, feature extraction, training the classifier, and classification. The work also included hardware design and implementation, as well as software programming for the microcontroller unit of the detector. The system utilized the microcontroller to receive and send data from the various sensors to an online database.

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Published

19-06-2023

How to Cite

Mathew, A. P., Sreehari, Viswajith, P., Rahman, A., & Murali, V. (2023). Crop Disease Detection using Machine Learning. Journal of Applied Science, Engineering, Technology and Management, 1(01), 28–32. https://doi.org/10.61779/jasetm.v1i1.6