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.

References

C K Sunil, C D Jaidhar and N Patil, Cardamom Plant Disease Detection Approach Using EfficientNetV2, in IEEE Access, vol. 10, pp. 789-804, 2022, doi: 10.1109/ACCESS.2021.3138920.

P. Sahu, A. P. Singh, A. Chug and D. Singh, A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop, in IEEE Access, vol. 10, pp. 87333-87360, 2022. doi: 10.1109/ACCESS.2022.3199926.

V. Rajesh Kumar, K. Pradeepan, S. Praveen, M. Rohith and V. Vasantha Kumar, Identification of Plant Diseases Using Image Processing and Image Recognition, 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 2021, pp. 1-4, doi: 10.1109/ICSCAN53069.2021.952649.

A. Devaraj, K. Rathan, S. Jaahnavi and K. Indira, Identification of Plant Disease using Image Processing Technique, 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0749-0753, doi: 10.1109/ICCSP.2019.8698056.

Chenghai Yang, Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example, Engineering, 6(5), 2020, pp 528-532. doi:10.1016/j.eng.2019.10.015

S. K. Pilli, B. Nallathambi, S. J. George and V. Diwanji, eAGROBOT — A robot for early crop disease detection using image processing, 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, 2015, pp. 1684-1689, doi: 10.1109/ECS.2015.7124873.

Orchi, H.; Sadik, M.; Khaldoun, M. On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey. Agriculture 2022, 12, 9. https://doi.org/10.3390/agriculture12010009

Ouhami, M.; Hafiane, A.; Es-Saady, Y.; El Hajji, M.; Canals, R. Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research. Remote Sens. 2021, 13, 2486. https://doi.org/10.3390/rs13132486.

Fang, Y.; Ramasamy, R.P. Current and Prospective Methods for Plant Disease Detection. Biosensors 2015, 5, pp 537-561. https://doi.org/10.3390/bios5030537

O. Kulkarni, Crop Disease Detection Using Deep Learning, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-4, doi: 10.1109/ICCUBEA.2018.8697390.

Anuradha Badage. Crop Disease Detection using Machine Learning: Indian Agriculture, International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 09 | Sep 2018, pp 186-189.

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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

Issue

Vol. 1 No. 01 (2023): Journal of Applied Science, Engineering, Technology and Management

Section

Articles

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Copyright (c) 2023 Abi P Mathew

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