Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning

Diana Susan Joseph, Pranav M. Pawar, Kaustubh Chakradeo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Agriculture plays a significant role in meeting food needs and providing food security for the increasingly growing global population, which has increased by 0.88% since 2022. Plant diseases can reduce food production and affect food security. Worldwide crop loss due to plant disease is estimated to be around 14.1%. The lack of proper identification of plant disease at the early stages of infection can result in inappropriate disease control measures. Therefore, the automatic identification and diagnosis of plant diseases are highly recommended. Lack of availability of large amounts of data that are not processed to a large extent is one of the main challenges in plant disease diagnosis. In the current manuscript, we developed datasets for food grains specifically for rice, wheat, and maize to address the identified challenges. The developed datasets consider the common diseases (two bacterial diseases and two fungal diseases of rice, four fungal diseases of maize, and four fungal diseases of wheat) that affect crop yields and cause damage to the whole plant. The datasets developed were applied to eight fine-tuned deep learning models with the same training hyperparameters. The experimental results based on eight fine-tuned deep learning models show that, while recognizing maize leaf diseases, the models Xception and MobileNet performed best with a testing accuracy of 0.9580 and 0.9464 respectively. Similarly, while recognizing the wheat leaf diseases, the models MobileNetV2 and MobileNet performed best with a testing accuracy of 0.9632 and 0.9628 respectively. The Xception and Inception V3 models performed best, with a testing accuracy of 0.9728 and 0.9620, respectively, for recognizing rice leaf diseases. The research also proposes a new convolutional neural network (CNN) model trained from scratch on all three food grain datasets developed. The proposed model performs well and shows a testing accuracy of 0.9704, 0.9706, and 0.9808 respectively on the maize, rice, and wheat datasets.

Original languageEnglish
Pages (from-to)16310-16333
Number of pages24
JournalIEEE Access
StatePublished - 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Deep learning
  • convolutional neural network
  • dataset development
  • plant disease classification
  • transfer learning


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