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An Offline Biotic Stress Recognition Tool for Rice Plants Through Domain Shift

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Abstract

Researchers are investigating deep learning techniques with their automatic feature learning capabilities for automated rice disease recognition from images. The current study has developed an ensamble model exploring hybrid features i.e., hand crafted and deep features. The proposed approach utilizes a laboratory image dataset comprising 2370 rice leaf images sourced from PlantVillage, augmented with another rice disease dataset featuring the same classes for training. These classes include BrownSpot, Leaf Blast, Hispa, and Healthy images in the dataset. The model achieves an accuracy of 97.9% through k-fold cross-validation. Considering the domain shift concept, we have tested the model’s accuracy on our real field rice leaf images containing BrownSpot, Leaf Blast, and Healthy leaves. The model achieves an accuracy of 93.7% on our dataset. To give the access of automatically identifying rice disease to the village farmers having poor internet connectivity, the current work introduces “easy to use” mobile application, RiceDiseaseRec. This research paves the way for automated rice disease recognition which leads to improving food security and mitigating crop yield losses.

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Data availability

The data that support the findings of this study are partially available in [22]. Also, the datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. We have utilized SDK API32 (Android API 32, SV2) with min. SDK 29 for reproducibility. Gradle 7.3.3 and Android Gradle Plugin 7.2.1 are used. Python 3.7.6, TensorFlow 2.3.1, NumPy 1.18.5, Pandas 1.0.5, Scikit-learn 0.23.1 were have employed for deep learning in Google Colab. Hardware: Intel Core i7-9750H CPU, 16 GB RAM. The performance of RiceDiseaseRec is demonstrated on various Android platforms with installation times ranging from 4.85 to 12.21 s and execution times ranging from 0.05 to 0.08 s.

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Acknowledgements

This work is supported by Ministry of Electronics and Information Technology (MeitY), R & D in IT Group, ITEA Division, Govt. of India. The authors also wish to thank Namballa Mukesh for helping in developing the mobile based application and Dr. Imon Mukherjee for providing reviews for improving the quality of the paper.

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  1. Computer Science and Engineering, Indian Institute of Information Technology Kalyani, Kalyani, 741235, India

    Chiranjit Pal, Sanjay Chatterji & Sanjoy Pratihar

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  1. Chiranjit Pal
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  2. Sanjay Chatterji
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Correspondence to Chiranjit Pal.

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Pal, C., Chatterji, S. & Pratihar, S. An Offline Biotic Stress Recognition Tool for Rice Plants Through Domain Shift. SN COMPUT. SCI. 5, 478 (2024). https://doi.org/10.1007/s42979-024-02816-2

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