Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms

Authors

  • Fatema Nihar Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Nazmun Nahar Khanom Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Syed Sahariar Hassan Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
  • Amit Kumar Das Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh

DOI:

https://doi.org/10.38032/jea.2021.01.007

Keywords:

Artificial Neural Network, Agriculture, Disease Detection, Monitoring

Abstract

In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way.  If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.

References

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Published

12-03-2021
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How to Cite

Nihar, F., Khanom, N. N., Hassan, S. S., & Das, A. K. (2021). Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms. Journal of Engineering Advancements, 2(01), 48–57. https://doi.org/10.38032/jea.2021.01.007
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