Methods and systems for pattern characteristic detection
First Claim
1. A method to detect pattern characteristics in target specimens, the method comprising:
- acquiring sensor data for the target specimens;
dividing the acquired sensor data into a plurality of data segments;
generating, by multiple neural networks that each receives the plurality of data segments, multiple respective output matrices, with each data element of the multiple respective output matrices being representative of a probability that corresponding sensor data of a respective one of the plurality of data segments includes a pattern characteristic in the target specimens; and
determining by another neural network, based on the multiple respective output matrices generated by the multiple neural networks, a presence of the pattern characteristic in the target specimens;
wherein the method further comprises;
providing training image data to train the multiple neural networks and the other neural network to detect northern leaf blight (NLB) disease in corn crops; and
training the multiple neural networks and the other neural network using the training image data, including;
identifying a lesion with a lesion axis in at least one image of the corn crops from the image data;
defining multiple image segments of predetermined dimensions that are each shifted, from another of the multiple image segments, by a predetermined length of pixels and having a center at a randomly selected location within a predetermined radius of pixels from the lesion axis of the lesion; and
rotating the each of the defined multiple image segments by a random rotation angle.
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Abstract
Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method to detect pattern characteristics in target specimens that includes acquiring sensor data for the target specimens, dividing the acquired sensor data into a plurality of data segments, and generating, by multiple neural networks that each receives the plurality of data segments, multiple respective output matrices, with each data element of the multiple respective output matrices being representative of a probability that corresponding sensor data of a respective one of the plurality of data segments includes a pattern characteristic in the target specimens. The method further includes determining by another neural network, based on the multiple respective output matrices generated by the multiple neural networks, a presence of the pattern characteristic in the target specimens.
20 Citations
18 Claims
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1. A method to detect pattern characteristics in target specimens, the method comprising:
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acquiring sensor data for the target specimens; dividing the acquired sensor data into a plurality of data segments; generating, by multiple neural networks that each receives the plurality of data segments, multiple respective output matrices, with each data element of the multiple respective output matrices being representative of a probability that corresponding sensor data of a respective one of the plurality of data segments includes a pattern characteristic in the target specimens; and determining by another neural network, based on the multiple respective output matrices generated by the multiple neural networks, a presence of the pattern characteristic in the target specimens; wherein the method further comprises; providing training image data to train the multiple neural networks and the other neural network to detect northern leaf blight (NLB) disease in corn crops; and training the multiple neural networks and the other neural network using the training image data, including; identifying a lesion with a lesion axis in at least one image of the corn crops from the image data; defining multiple image segments of predetermined dimensions that are each shifted, from another of the multiple image segments, by a predetermined length of pixels and having a center at a randomly selected location within a predetermined radius of pixels from the lesion axis of the lesion; and rotating the each of the defined multiple image segments by a random rotation angle. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A detection system to detect pattern characteristics in target specimens, the system comprising:
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one or more sensors to acquire sensor data for the target specimens; a controller to divide the acquired sensor data into a plurality of data segments; multiple neural networks, each configured to receive the plurality of data segments and to generate multiple respective output matrices, with each data element of the multiple respective output matrices being representative of a probability that corresponding sensor data of a respective one of the plurality of data segments includes a pattern characteristic in the target specimens; and at least one other neural network configured to determine, based on the multiple respective output matrices generated by the multiple neural networks, a presence of the pattern characteristic in the target specimens; wherein the controller is further configured to; provide training image data to train the multiple neural networks and the other neural network to detect northern leaf blight (NLB) disease in corn crops; and train the multiple neural networks and the other neural network using the training image data, including to; identify a lesion with a lesion axis in at least one image, of the corn crops, from the image data; define multiple image segments, from the at least one image, of predetermined dimensions that are each shifted, from another of the multiple image segments, by a predetermined length of pixels and having a center at a randomly selected location within a predetermined radius of pixels from the lesion axis of the lesion; and rotate the each of the defined multiple image segments by a random rotation angle. - View Dependent Claims (13, 14, 15, 16, 17)
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18. A non-transitory computer readable media storing a set of instructions, executable on at least one programmable device, to:
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acquire sensor data for target specimens; divide the acquired sensor data into a plurality of data segments; generate, by multiple neural networks that each receives the plurality of data segments, multiple respective output matrices, with each data element of the multiple respective output matrices being representative of a probability that corresponding sensor data of a respective one of the plurality of data segments includes a pattern characteristic in the target specimen; and determine by another neural network, based on the multiple respective heatmaps generated by the multiple neural networks, a presence of the abnormality in the target specimens; wherein the set of instructions comprises one or more further instructions, executable on the at least one programmable device, to further; provide training image data to train the multiple neural networks and the other neural network to detect northern leaf blight (NLB) disease in corn crops; and train the multiple neural networks and the other neural network using the training image data, including to; identify a lesion with a lesion axis in at least one image, of the corn crops, from the image data; define multiple image segments, from the at least one image, of predetermined dimensions that are each shifted, from another of the multiple image segments, by a predetermined length of pixels and having a center at a randomly selected location within a predetermined radius of pixels from the lesion axis of the lesion; and rotate the each of the defined multiple image segments by a random rotation angle.
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Specification