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Detecting and Counting Soybean Aphids Using Convolutional Neural Network
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1800317.(doi:10.13031/aim.201800317)
Authors: Kai-Yang Hsieh, Yan-Fu Kuo, Chuan-Kai Kuo
Keywords: Convolutional neural network, Aphid, Count, Pest management, Economic threshold
Abstract. The soybean aphid (Aphis glycines) is a major pest of soybean, one of the most important crops worldwide. It has been reported to substantially reduce soybean growth and production in Asia and North America. Soybean pest management typically applies pesticides to control aphid populations when the aphid density reaches the economic threshold of 250 aphids per plant. Hence, precise estimation of aphid population density becomes an essential component in effective pest management. Conventional manual counting for estimating aphid population size is laborious and prone to human error (e.g., variation in experience). To improve the counting efficiency that is critical to effective pest management, this study proposed to detect and count aphid numbers automatically using machine vision and deep learning. We first cultivated aphids on soybean plants in environmental chambers. Images of soybean leaves were acquired and segmented into small patches. A convolutional neural network classifier was then developed to differentiate the patches into 3 classes: winged aphids (high colonization ability), wingless aphid (high reproductive ability), and background. The proposed method was compared to the conventional manual method for evaluating its accuracy.
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