Intelligent Disease Detection in Sugarcane Plants: A Comparative Analysis of Machine Learning Models for Classification and Diagnosis

Authors

  • Santhrupth B. C. Research Scholar, Jain University, Bangalore, Assistant Professor, Dept. of CSE, Christ (Deemed to be) University, Bangalore
  • Devaraj Verma C. Professor, Dept, of CSE, Jain (Deemed to be) University, Bangalore

Keywords:

Sugarcane, Leaf Diseases, Convolution Neural Network (CNN), Random Forests, Support Vector Machine, Image Processing, Machine Learning, Texture Analysis, Disease Classification, Crop Health, Agricultural Intelligence

Abstract

Sugarcane is an important crop, but its production is hampered by problems such as water shortages and disease. This article presents a machine learning-based approach to accurately detect and classify sugarcane leaf diseases using convolutional neural networks (CNNs), random forests, and support vector machine models. This study focuses on the global importance of sugarcane, the prevalence of sugarcane in medical applications, and the main cultivation regions. The implementation includes the use of Python programming and deep learning algorithms, specifically his SVM, random forest, and CNN to classify sugarcane leaf diseases based on color, texture, and shape features. This process includes data collection, local binary patterns, texture analysis using Gabor filters and his GLCM, and disease classification.

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References

Alencastre-Miranda, M., Johnson, R.M. and Krebs, H.I., 2020. Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties. IEEE Transactions on Industrial Informatics, 17(2), pp.787-794.

Alencastre-Miranda, M., Johnson, R.M. and Krebs, H.I., 2020. Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties. IEEE Transactions on Industrial

Elavarasan, D., Vincent, D.R., Sharma, V., Zomaya, A.Y. and Srinivasan, K., 2018. Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and electronics in agriculture, 155, pp.257-282.

Geetha, G., Samundeswari, S., Saranya, G., Meenakshi, K. and Nithya, M., 2020, December. Plant leaf disease classification and detection system using machine learning. In Journal of Physics: Conference Series (Vol. 1712, No. 1, p. 012012). IOP Publishing.

Informatics, 17(2), pp.787-794.

Narmilan, A., Gonzalez, F., Salgadoe, A.S.A. and Powell, K., 2022. Detection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images. Drones, 6(9), p.230.

Shakoor, N., Lee, S. and Mockler, T.C., 2017. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current opinion in plant biology, 38, pp.184-192.

Sinha, A. and Shekhawat, R.S., 2020. Review of image processing approaches for detecting plant diseases. IET Image Processing, 14(8), pp.1427-1439.

Thilagavathi, K., Kavitha, K., Praba, R.D., Arina, S.V. and Sahana, R.C., 2020. Detection of diseases in sugarcane using image processing techniques. Bioscience Biotechnology Research Communications, Special Issue, (11), pp.109-115.

Usha Rani, M., Saravana Selvam, N. and Jegatha Deborah, L., 2022. An improvement of yield production rate for crops by predicting disease rate using intelligent decision systems. Int. J. Softw. Sci. Comput. Intell.(IJSSCI), 14(1), pp.1-22.

Verma, A.K., Garg, P.K. and Hari Prasad, K.S., 2017. Sugarcane crop identification from LISS IV data using ISODATA, MLC, and indices based decision tree approach. Arabian Journal of Geosciences, 10, pp.1-17.

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Published

13.12.2023

How to Cite

B. C., S. ., & Verma C., D. . (2023). Intelligent Disease Detection in Sugarcane Plants: A Comparative Analysis of Machine Learning Models for Classification and Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 299–306. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4121

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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