Method of Anomalies Detection in Persea Americana Leaves with Thermal and NGRDI Imagery

Rivadeneyra Bustamante B.D.; Huamán Bustamante S.G. · Smart Innovation, Systems and Technologies (2021) · DOI: 10.1007/978-3-030-57566-3_28

Resumen

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.

Instituciones: Universidad Nacional de Ingeniería