AN IDENTIFICATION METHOD OF PINUS MASSONIANA PEST AREA USING IMPROVED GOOGLENET
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The recent years have witnessed the increase of forest biological disasters. The conventional identification methods of Pinus massoniana pest area have the problem of low accuracy. With the help of artificial intelligence and big data technology, this paper proposes an identification method of Pinus massoniana pest area based on improved GoogLeNet. First, five features of Pinus massoniana images are extracted: Color and texture features are extracted respectively using color moments and gray level co-occurrence matrix, and three spectral features are extracted from the relative spectral reflec
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AN IDENTIFICATION METHOD OF PINUS MASSONIANA PEST AREA USING IMPROVED GOOGLENET
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