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MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

Bobbinpreet1, Sultan Aljahdali2,*, Tripti Sharma1, Bhawna Goyal1, Ayush Dogra3, Shubham Mahajan4, Amit Kant Pandit4

1 Department of Electronics & Communication Engineering, Chandigarh University, Mohali, 140413, India
2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Ronin Institute, Mont Clair, NJ, 07043, USA
4 School of Electronics & Communication Engineering, Shri Mata Vaishno Devi University, Katra, 182320, India

* Corresponding Author: Sultan Aljahdali. Email: email

Computers, Materials & Continua 2022, 72(3), 4771-4787.


In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.


Cite This Article

APA Style
Bobbinpreet, , Aljahdali, S., Sharma, T., Goyal, B., Dogra, A. et al. (2022). MRMR based feature vector design for efficient citrus disease detection. Computers, Materials & Continua, 72(3), 4771-4787.
Vancouver Style
Bobbinpreet , Aljahdali S, Sharma T, Goyal B, Dogra A, Mahajan S, et al. MRMR based feature vector design for efficient citrus disease detection. Comput Mater Contin. 2022;72(3):4771-4787
IEEE Style
Bobbinpreet et al., "MRMR Based Feature Vector Design for Efficient Citrus Disease Detection," Comput. Mater. Contin., vol. 72, no. 3, pp. 4771-4787. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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