ICTACT Journal on Soft Computing

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Classification of Paddy Leaf Diseases With Extended Huber Loss Function Using Convolutional Neural Networks


Affiliations
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India., India
2 Department of Computer Science, Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, India., India
     

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Paddy is a major food crop serving more than half the population of people in the world. It is inevitable to improve the quantity and quality of food crop with the growing population. Different factors including soil fertility, water availability, erratic climate variations, diseases, and pests, have an impact on paddy crop yield. It is crucial to identify the root cause for the reduction in yield of paddy. Early disease diagnosis prevents the plants from getting worst through its consecutive stage. The concern with manually diagnosing plant leaf diseases with the naked eye is that the results can be less accurate and even unreliable. Automatic disease diagnosis eliminates the need for experts and provides accurate results. This paper will assist the farmers to identify the leaf diseases automatically with the aid of Convolutional Neural Networks. This research includes paddy leaf disease categories: bacterial blight, blast, tungro, brown spot and healthy leaves. The dataset contains 800 images, 160 images from each of the five categories. Images are resized to 256 * 256 pixels and normalized. The network architecture created with convolutional, maxpooling, flatten and dense layers. The Dataset is divided into training and validation set in 70:30 ratios and model is trained with 20 epochs of batch size 16. The novelty of the study is the implementation of extended Huber loss function for minimizing the loss. Furthermore, it is cross compared with existing loss functions. The Proposed model has achieved 96.63% training accuracy and 86.61% validation accuracy with 5 classes. Performance of model is evaluated with confusion matrix with precision, recall, F1-score and support as parameters.

Keywords

Paddy Disease Detection, Preprocessing, Classification, Huber Loss, Convolutional Neural Network.
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  • Agriculture and Allied Industries, Available at https://www.ibef.org/download/1658816319_Agricultureand-Allied-Industries-June-2022.pdf, Accessed in 2023.-

  • R. Sharma and M. Pandey, “A Model for Prediction of Paddy Crop Disease using CNN”, Proceedings of International Conference on Progress in Computing, Analytics and Networking, pp. 533-543, 2020.

  • G. Shrestha and N. Dey, “Plant Disease Detection using CNN”, Proceedings of International Conference on Applied Signal Processing, pp. 109-113, 2020.

  • P. Tejaswini, Y.K. Rathore and R.R. Janghel, “Rice Leaf Disease Classification using CNN”, Proceedings of International Conference on Earth and Environmental Science, pp. 12017-12023, 2022.

  • G. Saini and A.K. Luhach, “Classification of Plants using Convolutional Neural Network”, Proceedings of International Conference on Sustainable Technologies for Computational Intelligence, pp. 547-558, 2020.

  • R. Swathika and K. Sowmya, “Disease Identification in Paddy Leaves using CNN based Deep Learning”, Proceedings of International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, pp. 1004-1008, 2021.

  • M.A. Islam and T. Khatun, “An Automated Convolutional Neural Network based Approach for Paddy Leaf Disease Detection”, International Journal of Advanced Computer Science and Applications, Vol. 12, No. 1, pp. 1-13, 2021.

  • B.S. Bari, A.F. Ab Nasir and M. Majeed, “A Real-Time Approach of Diagnosing Rice Leaf Disease using Deep Learning-based Faster R-CNN Framework”, Peer Journal on Computer Science, Vol. 7, pp. 432-443, 2021.

  • M. Sibiya and M. Sumbwanyambe, “A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves using Convolutional Neural Networks”, AgriEngineering, Vol. 1, No. 1, pp. 119- 131, 2019.

  • G. Shrivastava and H. Patidar, “Rice Plant Disease Identification Decision Support Model using Machine Learning”, ICTACT Journal on Soft Computing, Vol. 12, No. 3, pp. 2619-2627, 2022.

  • H.D. Nayak and A.K. Sarvaiya, “Facial Expression Recognition based on Feature Enhancement and Improved Alexnet”, ICTACT Journal on Soft Computing, Vol. 12, No. 3, pp. 2589-2600, 2022.

  • K. Janocha and W.M. Czarnecki, “On Loss Functions for Deep Neural Networks in Classification”, Proceedings of International Conference on Progress in Computing and Analytics, pp. 1-7, 2022.

  • G. Latif and Z.A. Kazimi, “Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases using an Improved CNN Model”, Plants, Vol. 11, No. 17, pp. 2230- 2243, 2022.

  • P.S. Thakur and A. Ojha, “VGG-ICNN: A Lightweight CNN Model for Crop Disease Identification”, Multimedia Tools and Applications, Vol. 87, pp. 1-24, 2022.

  • S.I. Prottasha and S.M.S. Reza, “A Classification Model based on Depthwise Separable Convolutional Neural Network to Identify Rice Plant Diseases”, International Journal of Electrical and Computer Engineering, Vol. 12, No. 4, pp. 1-12, 2022.

  • S.M. Hassan and E. Jasinska, “Identification of Plant-Leaf Diseases using CNN and Transfer-Learning Approach”, Electronics, Vol. 10, No. 12, pp. 1388-1398, 2021.

  • G. Geetharamani and A. Pandian, “Identification of Plant Leaf Diseases using a Nine-Layer Deep Convolutional Neural Network”, Computers and Electrical Engineering, Vol. 76, pp. 323-338, 2019.

  • P. Kaur and A.M. Alabdali, “Recognition of Leaf Disease using Hybrid Convolutional Neural Network by Applying Feature Reduction”, Sensors, Vol. 22, No. 2, pp. 575-584, 2022.

  • G.P. Meyer, “An Alternative Probabilistic Interpretation of the Huber Loss”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 5261-5269, 2021.

  • Mendeley Data, “Rice Leaf Disease Image Samples”, Available at https://www.kaggle.com/datasets/minhhuy2810/ricediseases-image-dataset, Accessed in 2021.


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  • Classification of Paddy Leaf Diseases With Extended Huber Loss Function Using Convolutional Neural Networks

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Authors

B. Sowmiya
PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India., India
K. Saminathan
PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India., India
M.Chithra Devi
Department of Computer Science, Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, India., India

Abstract


Paddy is a major food crop serving more than half the population of people in the world. It is inevitable to improve the quantity and quality of food crop with the growing population. Different factors including soil fertility, water availability, erratic climate variations, diseases, and pests, have an impact on paddy crop yield. It is crucial to identify the root cause for the reduction in yield of paddy. Early disease diagnosis prevents the plants from getting worst through its consecutive stage. The concern with manually diagnosing plant leaf diseases with the naked eye is that the results can be less accurate and even unreliable. Automatic disease diagnosis eliminates the need for experts and provides accurate results. This paper will assist the farmers to identify the leaf diseases automatically with the aid of Convolutional Neural Networks. This research includes paddy leaf disease categories: bacterial blight, blast, tungro, brown spot and healthy leaves. The dataset contains 800 images, 160 images from each of the five categories. Images are resized to 256 * 256 pixels and normalized. The network architecture created with convolutional, maxpooling, flatten and dense layers. The Dataset is divided into training and validation set in 70:30 ratios and model is trained with 20 epochs of batch size 16. The novelty of the study is the implementation of extended Huber loss function for minimizing the loss. Furthermore, it is cross compared with existing loss functions. The Proposed model has achieved 96.63% training accuracy and 86.61% validation accuracy with 5 classes. Performance of model is evaluated with confusion matrix with precision, recall, F1-score and support as parameters.

Keywords


Paddy Disease Detection, Preprocessing, Classification, Huber Loss, Convolutional Neural Network.

References