Main Article Content


Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases.

Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease.

Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology.

Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural.

The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm.


Soybean Soybean Plant Disease Image Processing

Article Details

How to Cite
Singh Rajput, A., Shukla, S., & S. Thakur, S. (2020). SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES. International Journal of Students’ Research in Technology & Management, 8(3), 01-08.


  1. Akhtar, Asma, Khanum, A., Khan, S.A. & Shaukat, A. (2013). Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques. IEEE International Conference on Frontiers of Information Technology (FIT), pp. 60-65.
  2. Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M. & Rahamneh, Z. A. (2011). Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications. 17(1), pp 31-38.
  3. Arivazhagan, S. & Newlin, S. R. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features, Agricultural Engineering Institute: CIGR journal, 15(1).
  4. Athanikar, Girish, & Badar, P. (2016). Potato leaf diseases detection and classification system. International Journal of Computer Science and Mobile Computing, 5.2, pp. 76-88.
  5. Bhong, Vijay, S. & Pawar, B. V. (2016). Study and Analysis of Cotton Leaf Disease Detection Using Image Processing. International Journal of Advanced Research in Science, Engineering and Technology.ssss
  6. Ferentinos, K. P. (2018), Deep learning models for plant disease detection and diagnosis. Computer And Electronics in Agriculture, Elsevier, 145, pp 311- 318.
  7. Gonzalez, R. C. & Woods, R. E., Digital Image Processing, Pearson Education, Third Edition.
  8. Gavhale, K. R., Gawande, U. & Hajari, K. O. (2014). Unhealthy Region of Citrus Leaf Detection Using Image Processing Techniques. IEEE, International Conference for Convergence of Technology, Pune, pp 1-6.
  9. Guo, J., Zhou, J., Qin, H., Zou, Q. & Li, Q. (2011). Monthly stream flow forecasting based on improved support vector machine model. Expert Syst. Appl. 38, 13073–13081.
  10. Haralick, R. M. (1973). Textural Features for Image Classification. IEEE transactions on Systems, 3(6), pp 610-621.
  11. Jalal, S., Singh, A., Dubey, R. & Ram, S. (2012). Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns. IEEE Third International Conference on Computer and Communication Technology, pp. 978-0-7695-4872.
  12. Kanjalkar, H. P. & Lokhande, S. (2013). Detection and Classification of Plant Leaf Diseases using ANN. International Journal of Scientific & Engineering Research, ISSN: 2229 5518.
  13. Khirade, S. & Patil, A. B. (2015). Plant Disease Detection Using Image Processing. IEEE, International Conference on Computing Communication Control and Automation, Pune, pp 768-771.
  14. Kutty, S. B., Abdullah, N.E., Hashim, A. H., Zraa, A., Rahim, A., Aida Sulinda Kusim,Tuan. Norjihan Tuan Yaakub, Puteri Nor Ashikin, Megat Yunus, Mohd Fauzi Abd Rahman (2013), Classification of Watermelon Leaf Diseases Using Neural Network Analysis, IEEE, Business Engineering and Industrial Applications Colloquium (BEIAC), Langkawi, pp 459 – 464.
  15. Mokhtar, Usama, Alit, Mona A. S., Hassenian, A. E. & Hesham, H. (2015). Tomato leaves diseases detection approach based on support vector machines. IEEE, pp. 978-1-5090-0275-7/15.
  16. Mollazade, K., Omid, M., Akhlaghian, F., Kalaj, Y. R., Mohtasebi, S. S. & Zude, M. (2013). Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light. Computers and Electronics in Agriculture, Elsevier, 98, 34-45.
  17. Priya, P., Dony, A. & Dsouza, A. (2015). Study of Feature Extraction Techniques for the Detection of Diseases of Agricultural Products. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 3(2).
  18. Sannaki, S., S., Rajpurohit, V. S., Nargund, V. B. & Kulkarni, P. (2013). Diagnosis and Classification of Grape Leaf Diseases using Neural Network. IEEE, Tiruchengode, pp 1 – 5.
  19. Shinde, B. T. S. (2018). Improved K-means Algorithm for Searching Research Papers. International Journal of Computer Science & Communication Networks, vol. 4, pp. 197-202.
  20. Yasikka, M. & Santhi, E. (2015). Foliage Measurement Using Image Processing Techniques. International Journal for Trends in Engineering and Technology, 5(2).