Abstract
Multispectral imaging has become a useful means of sensing in increasing number of fields, including precision agriculture due to its many advantages such as being non-invasive and extracting additional information than the visible band. Combined with machine learning, such systems can further extract complex relationships among extracted spectral information to facilitate intelligent agriculture. This paper focuses on making the process of multispectral data analysis automatic for classifying virus infection in plants, specially cassava plants infected with brown streak virus and tomato plants infected with mottle virus. Multispectral images are sampled from cassava and tomato plants with a designed portable device and then passed on to the proposed automatic analysis process. Conventional methods often depend on time-consuming manual processes of cropping valid patches from sampled leaf images and then averaging the patches to obtain the spectral reflectance signatures of the leaves, prior to the classification stage. The developed automatic process can automatically extract valid leaf pixels from scanned leaf images and subsequent spectral information and integrate with the classification method for optimal detection of infected plants. It has not only reduced processing time and errors significantly but also make the entire process more optimal. Extensive experiments on cassava brown streak virus and tomato mottle virus have been conducted and results demonstrated the intended advantages of the developed process. Support vector machines with RBF kernel have been shown to perform well for the classification of uninfected, and infected classes. Experiments show that the application can offer less time-consuming automatic analysis of the captured data.
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Acknowledgments
Authors would like to thank Prof. Linda Hanley-Bowdoin and her team at the North Carolina State University for the provision of plant samples and related biological analysis. Halil Mertkan Sahin would also like to acknowledge the Scholarship provided by the Ministry of National Education of the Republic of Turkey.
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Sahin, H.M., Grieve, B., Yin, H. (2020). Automatic Multispectral Image Classification of Plant Virus from Leaf Samples. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_33
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