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• W4366091488 abstract "Aphids cause severe damage to agricultural crops, resulting in significant economic losses, and an increased use of pesticides with decreased efficiency. Monitoring aphid infestations through regular field surveys is time-consuming and does not always provide an accurate spatiotemporal representation of the distribution of pests. Therefore, an automated, non-destructive method to detect and evaluate aphid infestation would be beneficial for targeted treatments. In this study, we present a machine learning model to identify and quantify aphids, localizing their spatial distribution over leaves, using a One-Class Support Vector Machine and Laplacian of Gaussians blob detection. To train this model, we built the first large database of aphids’ hyperspectral images, which were captured in a controlled laboratory environment. This database contains more than 160 images of three aphid lines, distinctive in color, shape, and developmental stages, and are displayed laying on leaves or neutral backgrounds. This system exhibits high-quality validation scores, with a Precision of 0.97, a Recall of 0.91, an F1 score of 0.94, and an AUPR score of 0.98. Moreover, when assessing this method on new and challenging images, we did not observe any false negatives (and only a few false positives). Our results suggest that a machine learning model of this caliber could be a promising tool to detect aphids for targeted treatments in the field." @default.
• W4366091488 created "2023-04-19" @default.
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• W4366091488 date "2023-04-16" @default.
• W4366091488 modified "2023-10-18" @default.
• W4366091488 title "Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians" @default.
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• W4366091488 doi "https://doi.org/10.3390/rs15082103" @default.
• W4366091488 hasPublicationYear "2023" @default.
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