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Fractional weighted nuclear norm based two dimensional linear discriminant features for cucumber leaf disease recognition

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Abstract

The quality and quantity of agricultural products are significantly affected by plant diseases. The plant diseases could be mitigated if identified at an early stage. In general, the assessment of plant diseases is performed by human raters. There are some disadvantages in recognizing the plant disease in the leaves when observed by human raters. In this research work, an alternate machine learning based feature extraction technique named fractional weighted nuclear norm based two-dimensional linear discriminant analysis (FNN-2DLDA) is proposed to identify the cucumber leaf diseases. The proposed machine learning approach mainly comprises of three phases: firstly, the nuclear norm reveals the spatial relationship between pixels in each leaf image. Secondly, the fractional weight is assigned to weaken the effect of edge class problem (when a greater number of disease classes are present). Finally, the multi-class hierarchical support vector machine (HSVM) classifier is implemented to recognize the cucumber leaf diseases. The proposed machine learning based system contribute to protection and enhancement of leaf species thus resulting in environmental and Gross Domestic Product (GDP) improvement of the nation. The performance metrics, viz. similarity index, recognition rate, confusion matrix based statistical measurements and hypothesis test results of the proposed technique are compared with the existing start-of-the-art techniques. The disease recognition accuracy of the proposed techniques is 87.5%, 88%, 85% and also 95% of confidence in statistical hypothesis testing of unilateral and bilateral procedures. The results show that the proposed FNN-2DLDA technique outperforms the existing techniques which is attributed to the incorporation of nuclear norm and fractional weight into conventional 2DLDA.

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Acknowledgements

The authors would like to thank Dr. Shanwen Zhang, Department of Electronics and Information Engineering, XiJing University, Xi’an 710123, China for providing the access to use the cucumber leaf image database. Sincere thanks to Dr. S Domnic, Artificial Intelligence, Center of Excellence, Department of Computer Applications, National Institute of Technology Trichy, India for providing access to use the GPU workstation for simulations.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology Trichy, Tiruchirappalli, India

    G. Yogeswararao, R. Malmathanraj & P. Palanisamy

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  1. G. Yogeswararao
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  2. R. Malmathanraj
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  3. P. Palanisamy
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Correspondence to G. Yogeswararao.

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Yogeswararao, G., Malmathanraj, R. & Palanisamy, P. Fractional weighted nuclear norm based two dimensional linear discriminant features for cucumber leaf disease recognition. Multimed Tools Appl 81, 38735–38755 (2022). https://doi.org/10.1007/s11042-022-13013-9

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  • DOI: https://doi.org/10.1007/s11042-022-13013-9

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