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QUANTIFYING PLANT INFESTATION BY ESTIMATING THE NUMBER OF INSECTS ON LEAVES, BY CONVOLUTIONAL NEURAL NETWORKS THAT USE TRAINING IMAGES OBTAINED BY A SEMI-SUPERVISED APPROACH
QUANTIFYING PLANT INFESTATION BY ESTIMATING THE NUMBER OF INSECTS ON LEAVES, BY CONVOLUTIONAL NEURAL NETWORKS THAT USE TRAINING IMAGES OBTAINED BY A SEMI-SUPERVISED APPROACH
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机译:通过使用半导体方法获得的卷积神经网络估算叶片上的昆虫数量来量化植物侵扰
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摘要
A computer generates a training set with annotated images (473) to train a convolutional neural network (CNN). The computer receives leaf-images showing leaves and insects, in a first color-coding (413-A), changes the color-coding of the pixels to a second color-coding and thereby enhances the contrast (413-C), assigns pixels in the second color-coding to binary values (413-D), differentiates areas with contiguous pixels in the first binary value into non-insect areas and insect areas by an area size criterion (413-E), identifies pixel-coordinates of the insect areas with rectangular tile-areas (413-F), and annotates the leaf-images in the first color-coding by assigning the pixel-coordinates to corresponding tile-areas. The annotated image is then used to train the CNN for quantifying plant infestation by estimating the number of insects on the leaves of plants.
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