Automating yellow rust disease identification in wheat using artificial intelligence

SAPNA NIGAM; RAJNI JAIN; SUDEEP MARWAHA; ALKA ARORA; VAIBHAV KUMAR SINGH; AVESH KUMAR SINGH; RANJIT KUMAR PAUL; KINGSLY IMMANUELRAJ T

doi:10.56093/ijas.v91i9.116097

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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References

Barbedo J G A. 2018. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture 153: 46–53. DOI: https://doi.org/10.1016/j.compag.2018.08.013

Bashish A D, Braik M and Bani-Ahmad S. 2010. A framework for detection and classification of plant leaf and stem diseases. (In) Signal and Image Processing International Conference, IEEE.

Boulent J, Foucher S, Théau J and St-Charles P L. 2019. Convolutional neural networks for the automatic identification of plant diseases. Frontiers in plant science 10. DOI: https://doi.org/10.3389/fpls.2019.00941

DeChant C, Wiesner-Hanks T, Chen S, Stewart E L, Yosinski J, Gore M A and Lipson H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107(11): 1426–32 DOI: https://doi.org/10.1094/PHYTO-11-16-0417-R

Ferentinos K P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311–8. DOI: https://doi.org/10.1016/j.compag.2018.01.009

He, Kaiming, Xiangyu Zhang, Shaoqing Ren and Jian Sun. 2016. "Deep residual learning for image recognition." (In) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–78. DOI: https://doi.org/10.1109/CVPR.2016.90

Hungilo G G, Emmanuel G and Emanuel A W. 2019. Image processing techniques for detecting and classification of plant disease: A review. (In) Proceedings of the 2019 International Conference on Intelligent Medicine and Image Processing, pp 48–52. DOI: https://doi.org/10.1145/3332340.3332341

Jadhav S B, Udupi V R and Patil S B. 2020. Identification of plant diseases using convolutional neural networks. International Journal of Information Technology 1–10.

Jain R, Minz S and Ramasubramanian V. 2009. Machine learning for forewarning crop diseases. Journal of Indian Society of Agricultural Statistics 63(1): 97–107.

Kamilaris A and Boldú P F X. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147: 70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016

LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature 521(7553): 436. DOI: https://doi.org/10.1038/nature14539

Lee S H, Chan C S, Wilkin P and Remagnino P. 2015. Deep-plant: Plant identification with convolutional neural networks. (In) International Conference on Image Processing. IEEE, pp 452–56. DOI: https://doi.org/10.1109/ICIP.2015.7350839

Lu Y, Yi S, Zeng N, Liu Y and Zhang Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267: 378–84. DOI: https://doi.org/10.1016/j.neucom.2017.06.023

Mohanty S P, Hughes D P and Salathé M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7: 1419. DOI: https://doi.org/10.3389/fpls.2016.01419

Mokhtar U, El Bendary N, Hassenian A E, Emary E, Mahmoud M A, Hefny H and Tolba M F. 2015. SVM-based detection of tomato leaves diseases. Intelligent Systems 20(14): 641–52. DOI: https://doi.org/10.1007/978-3-319-11310-4_55

Nigam S and Jain R. 2020. Plant disease identification using Deep Learning: A review. Indian Journal of Agricultural Sciences 90(2): 249–57.

Patrício D I and Rieder R. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture 153: 69–81. DOI: https://doi.org/10.1016/j.compag.2018.08.001

Picon A, Alvarez-Gila A, Seitz M, Ortiz-barredo A, Echazarra J and Johannes A. 2018. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture 161: 280–90. DOI: https://doi.org/10.1016/j.compag.2018.04.002

Rumpf T, Mahlein A K, Steiner U, Oerke E C, Dehne H W and Plümer L. 2010. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74(1): 91–9. DOI: https://doi.org/10.1016/j.compag.2010.06.009

Schmidhuber J. 2015. Deep learning in neural networks: An overview. Neural Networks 61: 85–117. DOI: https://doi.org/10.1016/j.neunet.2014.09.003

Sladojevic, Srdjan, Marko Arsenovic, Andras Anderla, Dubravko Culibrk and Darko Stefanovic. 2016. "Deep neural networks based recognition of plant diseases by leaf image classification." Computational Intelligence and Neuroscience. DOI: https://doi.org/10.1155/2016/3289801

Too E C, Yujian L, Njuki S and Yingchun L. 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture 161: 272–79. DOI: https://doi.org/10.1016/j.compag.2018.03.032

Vaibhav K, Singh G P, Singh P K, Harikrishna R and Gogoi R. 2017. Assessment of slow rusting resistance components to stripe rust pathogen in some exotic wheat germplasm. Indian Phytopathology 70(1): 52–57. DOI: https://doi.org/10.24838/ip.2017.v70.i1.48994

Yang X and Guo T. 2017. Machine learning in plant disease research. European Journal of BioMedical Research 3(1): 6–9. DOI: https://doi.org/10.18088/ejbmr.3.1.2017.pp6-9