Implementation of a CNN-Based Model for Soybean Leaf Diseases
DOI:
https://doi.org/10.26438/ijsrnsc.v13i3.278Keywords:
Convolutional Neural Networks (CNNs), Machine Learning, Keras, TensorFlow, Plant Disease IdentificationAbstract
Soybean ranks among the most vital crops grown in India, especially in Madhya Pradesh, where it significantly contributes to the agricultural economy and supports nutritional security. Nonetheless, the yield of soybean crops is greatly affected by several leaf diseases, including bacterial blight, downy mildew, soybean rust, southern blight, and powdery mildew. Timely and precise detection of these diseases is crucial to reduce crop damage. Conventional disease identification techniques are often slow, require considerable manual effort, and are susceptible to human error. To address this issue, this study proposes the implementation of a Convolutional Neural Network (CNN)-based deep learning model to detect and classify common soybean leaf diseases using image data. A total of 5,917 images were utilized in the dataset, divided into five disease categories and one healthy category. The dataset was preprocessed and augmented to enhance model performance and divided into training (70%), validation (10%), and testing (20%) sets. The CNN model was trained over 20, 40, and 50 epochs to assess its performance across varying training durations. It demonstrated high classification accuracy, highlighting its effectiveness as a dependable method for the early detection of soybean leaf diseases.
References
ICAR-Indian Institute of Soybean Research, “Advances in Soybean Disease Management,” Extension Bulletin 2023, no.3, pp. 1–15, 2023.
Pagalla, B. S., Reddy, M. B., Goud, M. G., Mohan, M., Hari, R. S., & Jeswanth, U., “Detection and Classification of Tangerine Diseases Using Image Processing,” International Journal of Research Publication and Reviews, Vol.5, Issue 5, pp.5384-5390, 2024.
H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh, “Fast and accurate detection and classification of plant diseases,” Int. J. Comput. Appl., Vol. 17, no. 1, pp. 31–38, Mar. 2011.
K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., Vol. 145, pp. 311–318, Jan. 2018, doi: 10.1016/j.compag.2018.01.009
G. Saradhambal, R. Dhivya, S. Latha, and R. Rajesh, “Plant disease detection and its solution using image classification,” Int. J. Pure Appl.Math., Vol. 119, no. 14, pp. 879–884, 2018
M. H. Chang, J. Y. Pyun, M. B. Ahmad, J. H. Chun, and J. A. Park, “Modified Color Co-occurrence Matrix for Image Retrieval,” International Conference on Natural Computation (ICNC), Vol. 3611, pp. 43–50, 2005.
B. S. Anami, N. N. M. Naveen, and N. G. Hanamaratti, “Behavior of HSI Color Co-Occurrence Features in Variety Recognition from Bulk Paddy Grain Image Samples,” International Journal of Signal Processing, Image Processing and Pattern Recognition (IJSIP), Vol. 8, no. 4, pp. 19–30, Apr. 2015.
H. Park, E. JeeSook, and S.-H. Kim, “Crops disease diagnosing using image-based deep learning mechanism,” Proc. 2nd Int. Conf. Comput. Netw. Commun. (CoCoNet’18), pp. 23–26, Dec. 2018.
K. Sai Susheel and R. Rajkumar, “A Comprehensive Review on Intelligent Techniques in Crop Pests and Diseases,” International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 11, no. 9, pp. 137-145, doi:
17762/ijritcc. v11i9.8328, Sep. 2023.
S. Liu, “Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models,” Remote Sensing, Vol.15, no. 2, pp. 453, doi: 10.3390/rs15020453, 2023.
Arjoune, Youness; Sugunaraj, Niroop; Peri, Sai; Nair, Sreejith V; Skurdal, Anton; Ranganathan, Prakash; Johnson, Burton, “Soybean cyst nematode detection and management: a review,” Plant Methods, Vol. 18, pp. 1-39, doi: 10.1186/s13007-022- 00933-8,2022.
D. O. Kiobia et al., “A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton,” Sensors, Vol. 23, no. 8, pp. 4127, doi: 10.3390/s23084127, 2023.
Verma, Himangi; Vidyarthi, Aditya; Chitre, Abhijit V; Wanjale, Kirti H; Anusha, M; Majrashi, Ali; Simon Karanja Hinga., “Local Binary Patterns Based on Neighbor-Center Difference Image for Color Texture Classification with Machine Learning Techniques,” Wireless Communications & Mobile Computing (Online), Vol. 2022, doi:10.1155/2022/1191492,2022.
F. Mauro, F. Ribeiro, J. Metrôlho, and R. Dionísio, “Algorithms and models for automatic detection and classification of diseases and pests in agricultural crops: A systematic review,” Applied Sciences, Vol. 13, no. 8, p. 4720, 2023. doi: 10.3390/app13084720
N. Thapliyal, S. Thapliyal, V. Kukreja, and S. Mehta, “Disruptive Tech in Agriculture: Federated Learning CNNs for Soybean Leaf Disease Classification,” in Proc. 2024 3rd Int. Conf. Innovation Technology (INOCON), Bangalore, India, Mar. 1–3, 2024. IEEE, doi: 10.1109/INOCON60754.2024.10511434.
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., Vol. 161, pp. 272–279, 2019. doi: 10.1016/j.compag.2018.03.032.
P. S. Madhumitaa, C. Ragavi, C. Kiranmayi, V. M, and P. Prabhavathy, “Drought and salinity stress classification in soybean crops: Comparative analysis of machine learning models,” in Proc. 2024 Int. Conf. Signal Process., Comput., Electron., Power Telecommun. (IConSCEPT), Karaikal, India, Jul. 2024. doi: 10.1109/IConSCEPT61884.2024.10627854.
M. Yu, X. Ma, H. Guan, and T. Zhang, “A diagnosis model of soybean leaf diseases based on improved residual neural network,” Chemometrics and Intelligent Laboratory Systems, Vol. 237, p. 104824, Jun. 2023. doi: 10.1016/j.chemolab.2023.104824.
K. Zhang, Q. Wu, and Y. Chen, “Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN,” Computers and Electronics in Agriculture, Vol. 183, p. 106064, Apr. 2021. doi: 10.1016/j.compag.2021.106064.
E. Jain and P. Sharma, “Deep learning-based soybean leaf disease classification using VGG16 and data augmentation techniques,” in Proc. 2024 Int. Conf. Cybernation Comput. (CYBERCOM), Dehradun, India, Nov. 2024. doi: 10.1109/CYBERCOM63683.2024.10803140.
R. Bharti, V. Srivastava, A. Bajpai, and S. Sahu, “Comparative analysis of potato leaf disease classification using CNN and ResNet50,” in Proc. 2024 Int. Conf. Data Sci. Appl. (ICoDSA), Jul. 2024. doi: 10.1109/ICoDSA62899.2024.10651649.
Y. Kashyap, S. S. Shrivastava, and R. Sharma, “An improved soybean foliar disease detection system using deep learning,” in Proc. 2022 IEEE Conf. Interdiscip. Approaches Technol. Manage. Social Innov. (IATMSI), Gwalior, India, Dec. 2022. doi: 10.1109/IATMSI56455.2022.10119330.
V. Solanki, R. Ahuja, V. Khullar, and S. Thapliyal, “Optimizing rose leaf disease detection performance via explainable transfer learning: A comparative analysis,” in Proc. 2024 Int. Conf. Electr. Electron. Comput. Technol. (ICEECT), Greater Noida, India, Aug. 2024. doi: 10.1109/ICEECT61758.2024.10739187.
Weizheng, S., Yachun, W., Zhanliang, C., and Hongda, W. (2008). Grading Method of Leaf Spot Disease Based on Image Processing. In Proceedings of the 2008 international Conference on Computer Science and Software Engineering – Vol.06 (Dec.12-14,2008). CSSE. IEEE Computer Society, Washington, DC, 491-494. doi: 2008.1649. http://dx.doi.org/10.1109/CSSE.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature, Vol. 585, no. 7825, pp. 357–362, Sep. 2020, doi: 10.1038/S41586-020-2649-2.

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