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Full Research Article

volume 37 issue 2 (june 2022) : 173-177,   Doi: 10.18805/BKAP467

A Transfer Learning Approach for Leaf Image based Classification of Healthy and Diseased Leaves

N
Nilakshi Devi
S
Shakuntala Laskar
1Department of Electrical and Electronics Engineering, Assam Don Bosco University, Azara-781 017, Assam, India.

  • Submitted21-02-2022|

  • Accepted03-06-2022|

  • First Online 20-06-2022|

  • doi 10.18805/BKAP467

Cite article:- Devi Nilakshi, Laskar Shakuntala (2022). A Transfer Learning Approach for Leaf Image based Classification of Healthy and Diseased Leaves. Bhartiya Krishi Anusandhan Patrika. 37(2): 173-177. doi: 10.18805/BKAP467.

ABSTRACT

Background: India is mainly an agriculturist country where agriculture is a very important sector for Indian economy. Different varieties of crops are grown in huge acres of land yielding sufficient quantity. But due to frequent attacks of pests and pathogens the plants gets infected and develops different diseases. These diseases affect the quality of crop production and put the consumer’s health at risk. Thus, it is important to keep a track on the health of the plants on a regular basis so that required action can be taken without wasting any time. Therefore the study aimed to propose a method for automatic classification of healthy and diseased plant leaves using transfer learning approach. 
Methods: Deep learning is gaining popularity among researchers due to its several advantages. Two approaches were used in deep learning: one is to build a model from scratch and the other to use pre-trained models (PTMs). This concept is known as transfer learning. It is based on the concept where the network learns from existing models and uses the knowledge to solve a completely different problem. These models are trained on huge datasets to solve a task that is similar to our desired problem. Mobile Net V2 has been used for our research problem and the beans dataset which is a publicly available dataset. 
Result: Mobile Net V2 is a very light weight model which is trained on more than 1.4 M images. We have downloaded the pre-trained model and fine tuned the last layer to solve our desired task. The results observed that in just only 30 epochs our proposed model could achieve a classification accuracy of 98%. Also, we have compared our proposed method with other existing methods in literature and found that our proposed method had higher classification accuracy than other existing methods.  

REFERENCES


  1. Aamir, A., Malik, H. (2019). Applications of Artificial Intelligence Techniques in Engineering. 20(1): 698.   

  2. Aurelin, G. (2019). Hands-on Machine Learning with Scikit- Learn, Keras and Tensor Flow, O’Reilly Publisher.

  3. Britannica Encyclopedia on Plant Disease. https://www.britannica.com/ plant disease (last checked on Jan 21/2022).

  4. Chen, Gao, Liu. (2019). Visual tea leaf disease recognition using a convolutional neural network model. Symmetry. 11(2): 343-350.  

  5. Dekne, Oreke, Schonbeck. (2004). Crop Production and Crop Protection. Elsevier Science Publisher.

  6. Durmu (2017). Disease detection on the leaves of the tomato plants by using deep learning. Proceedings of the 2017 6th International Conference on Agro-Geoinformatics. 

  7. Dang, Hassan, Kwak. (2017). Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Appl. Remote Sens. 11(3): 426-432.

  8. Eli, S., Luca, A., Thomas, V. (2020). Deep Learning with Pytorch. Manning Publishers.

  9. Francois, C. et al. (2017). Deep Learning with Phyton. Manning Publishers. 

  10. Fujita, Kagiwada, Iyatomi. (2016). Basic Investigation on a Robust and Practical Plant Diagnostic System. In Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). 989-992.

  11. Jin, W. (2018). Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sensors. 10(1): 395-420.   

  12. Ramcharan, M. (2017). Deep learning for image-based cassava disease detection. Front. Plant Sci. 8(2): 1852.  

  13. Sibiya, M., Sumbwanyambe. (2019). Computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineerin. 30(4): 119-131. 

  14. State of Agriculture in India-Analytical Reports. https://prsindia.org/ policy/analytical-reports/state-agriculture-india (last checked on Jan 21/2022).

  15. Sun, Z. (2019). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture. 20(3). 94-100.

  16. TianXiang, S., Dai, N., Qiu, XiP. (2020). Pre-trained models for natural language processing: A survey. Science China Technological Sciences. 63(1): 1872-1897.  
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    Full Research Article

    volume 37 issue 2 (june 2022) : 173-177,   Doi: 10.18805/BKAP467

    A Transfer Learning Approach for Leaf Image based Classification of Healthy and Diseased Leaves

    N
    Nilakshi Devi
    S
    Shakuntala Laskar
    1Department of Electrical and Electronics Engineering, Assam Don Bosco University, Azara-781 017, Assam, India.

    • Submitted21-02-2022|

    • Accepted03-06-2022|

    • First Online 20-06-2022|

    • doi 10.18805/BKAP467

    Cite article:- Devi Nilakshi, Laskar Shakuntala (2022). A Transfer Learning Approach for Leaf Image based Classification of Healthy and Diseased Leaves. Bhartiya Krishi Anusandhan Patrika. 37(2): 173-177. doi: 10.18805/BKAP467.

    ABSTRACT

    Background: India is mainly an agriculturist country where agriculture is a very important sector for Indian economy. Different varieties of crops are grown in huge acres of land yielding sufficient quantity. But due to frequent attacks of pests and pathogens the plants gets infected and develops different diseases. These diseases affect the quality of crop production and put the consumer’s health at risk. Thus, it is important to keep a track on the health of the plants on a regular basis so that required action can be taken without wasting any time. Therefore the study aimed to propose a method for automatic classification of healthy and diseased plant leaves using transfer learning approach. 
    Methods: Deep learning is gaining popularity among researchers due to its several advantages. Two approaches were used in deep learning: one is to build a model from scratch and the other to use pre-trained models (PTMs). This concept is known as transfer learning. It is based on the concept where the network learns from existing models and uses the knowledge to solve a completely different problem. These models are trained on huge datasets to solve a task that is similar to our desired problem. Mobile Net V2 has been used for our research problem and the beans dataset which is a publicly available dataset. 
    Result: Mobile Net V2 is a very light weight model which is trained on more than 1.4 M images. We have downloaded the pre-trained model and fine tuned the last layer to solve our desired task. The results observed that in just only 30 epochs our proposed model could achieve a classification accuracy of 98%. Also, we have compared our proposed method with other existing methods in literature and found that our proposed method had higher classification accuracy than other existing methods.  

    REFERENCES


    1. Aamir, A., Malik, H. (2019). Applications of Artificial Intelligence Techniques in Engineering. 20(1): 698.   

    2. Aurelin, G. (2019). Hands-on Machine Learning with Scikit- Learn, Keras and Tensor Flow, O’Reilly Publisher.

    3. Britannica Encyclopedia on Plant Disease. https://www.britannica.com/ plant disease (last checked on Jan 21/2022).

    4. Chen, Gao, Liu. (2019). Visual tea leaf disease recognition using a convolutional neural network model. Symmetry. 11(2): 343-350.  

    5. Dekne, Oreke, Schonbeck. (2004). Crop Production and Crop Protection. Elsevier Science Publisher.

    6. Durmu (2017). Disease detection on the leaves of the tomato plants by using deep learning. Proceedings of the 2017 6th International Conference on Agro-Geoinformatics. 

    7. Dang, Hassan, Kwak. (2017). Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Appl. Remote Sens. 11(3): 426-432.

    8. Eli, S., Luca, A., Thomas, V. (2020). Deep Learning with Pytorch. Manning Publishers.

    9. Francois, C. et al. (2017). Deep Learning with Phyton. Manning Publishers. 

    10. Fujita, Kagiwada, Iyatomi. (2016). Basic Investigation on a Robust and Practical Plant Diagnostic System. In Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). 989-992.

    11. Jin, W. (2018). Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sensors. 10(1): 395-420.   

    12. Ramcharan, M. (2017). Deep learning for image-based cassava disease detection. Front. Plant Sci. 8(2): 1852.  

    13. Sibiya, M., Sumbwanyambe. (2019). Computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineerin. 30(4): 119-131. 

    14. State of Agriculture in India-Analytical Reports. https://prsindia.org/ policy/analytical-reports/state-agriculture-india (last checked on Jan 21/2022).

    15. Sun, Z. (2019). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture. 20(3). 94-100.

    16. TianXiang, S., Dai, N., Qiu, XiP. (2020). Pre-trained models for natural language processing: A survey. Science China Technological Sciences. 63(1): 1872-1897.  
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