A Rice Pest Identification Method Based on Improved Residual Network
A recognition method and technology of pests, applied in the field of deep learning, can solve problems such as information loss, achieve high recognition accuracy, improve recognition accuracy, and enrich image feature information
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[0059] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0060] The rice pest identification method based on the improved residual network of the embodiment of the present invention comprises the following steps:
[0061] (1) Input training data set
[0062] In 2019, Wu Xiaoping and others published a large-scale pest recognition dataset IP102, and carried out professional image annotation work. The data set category is still hierarchical, divided into 8 major crop categories and 102 pest subcategories. IP102 is the largest pest identification data set so far, containing 75,000 pest samples, and its categories almost include the most common pest specie...
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