Automating yellow rust disease identification in wheat using artificial intelligence


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Authors

  • SAPNA NIGAM ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • RAJNI JAIN ICAR-National Institute for Agricultural Economics and Policy Research, New Delhi
  • SUDEEP MARWAHA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • ALKA ARORA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • VAIBHAV KUMAR SINGH ICAR-Indian Agricultural Research Institute, New Delhi
  • AVESH KUMAR SINGH Punjab Agricultural University, Ludhiana, Punjab
  • RANJIT KUMAR PAUL ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • KINGSLY IMMANUELRAJ T ICAR-National Institute for Agricultural Economics and Policy Research, New Delhi

https://doi.org/10.56093/ijas.v91i9.116097

Keywords:

Artificial Intelligence, Automated plant disease identification, Computer vision, Deep learning, Image processing, Wheat rust

Abstract

Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational systems for disease diagnosis would facilitate advancements in agriculture. The objective of this paper is to explore computer vision based Artificial Intelligence method for automating the identification of yellow rust disease and improve the accuracy of plant disease identification. The dataset of 2000 images of wheat leaf were collected in the real life experimental conditions of ICAR-Indian Agricultural Research Institute, New Delhi in the crop season during January-April, 2019. Based on our experiment, we propose a deep learning-based approach to detect healthy leaves and yellow rust infected leaves in the wheat crop. The experiments are implemented in python with PyCharm IDE, utilizing the Keras deep learning library backend with TensorFlow. The proposed model achieves 97.3% testing accuracy and 98.42% as the training accuracy. The accuracy of the developed model can be improved further by training it with larger size of the dataset in future. In future, accuracy of computer vision based AI models can be improved by using the larger size training datasets. Also, these models can be used for providing automatic advisory services to the farmers, thereby, adding much needed assistance to the overloaded extension experts.

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Submitted

2021-09-27

Published

2021-09-27

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Articles

How to Cite

NIGAM, S., JAIN, R., MARWAHA, S., ARORA, A., SINGH, V. K., SINGH, A. K., PAUL, R. K., & T, K. I. (2021). Automating yellow rust disease identification in wheat using artificial intelligence. The Indian Journal of Agricultural Sciences, 91(9), 1391–1395. https://doi.org/10.56093/ijas.v91i9.116097
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