Abstract
Among the main agricultural export products of Perú is the Persea Americana (Hass avocado). However, pests and deficiencies cause a decrease in avocado crop yield. For that reason, anomalous visual characteristics detection of a leaf has been studied for thermal and multispectral images. In this study, we propose a procedure for imagery acquisition and evaluate factors that can affect the acquisition of thermal imagery; furthermore, we propose a method for the extraction of characteristics to classify between a healthy leaf and a leaf with some visible anomalies that indicate some deficiency or pest. This method consists of the following stages: segmentation of thermal and NGRDI images, resampling, clustering with k-means algorithm and classification with SVM. Likewise, we test three cases of image acquisition to analyze the effects on statistical descriptors. Finally, the classification stage was tested with leaves processed; as a result, we got out an average accuracy of 82.67%, from ten experiments with the same set of images.
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Acknowledgements
The authors thank Innóvate Peru and the Ministry of Production of Peru for financing with contract No. 119-INNOVATEPERÚ-IDIBIO-2018 and the National Institute of Agrarian Innovation (INIA) for access to their experimental stations.
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Rivadeneyra Bustamante, B.D., Huamán Bustamante, S.G. (2021). Method of Anomalies Detection in Persea Americana Leaves with Thermal and NGRDI Imagery. In: Iano, Y., Arthur, R., Saotome, O., Kemper, G., Borges Monteiro, A.C. (eds) Proceedings of the 5th Brazilian Technology Symposium. Smart Innovation, Systems and Technologies, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-030-57566-3_28
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DOI: https://doi.org/10.1007/978-3-030-57566-3_28
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