Advances in new nondestructive detection and identification techniques of crop diseases based on deep learning

doi:10.3969/j.issn.1004-1524.2019.04.21

›› 2019, Vol. 31 ›› Issue (4): 669-676.DOI: 10.3969/j.issn.1004-1524.2019.04.21

• Review • Previous Articles

Advances in new nondestructive detection and identification techniques of crop diseases based on deep learning

WANG Yanxiang¹, ZHANG Yan¹,^{,^*, YANG Chengya¹, MENG Qinglong², SHANG Jing²}

1. College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China;
2. Research Center of Nondestructive Testing for Agricultural Products, Guiyang University, Guiyang 550005, China

Received:2018-08-15 Online:2019-04-25 Published:2019-04-19

Abstract

Abstract: The non-destructive testing and early identification of crop diseases is the key to the development of precision agriculture and ecological agriculture. With the progress of image acquisition and image processing technologies, advanced imaging detection technologies such as hyperspectral imaging and image analysis technologies based on deep learning were increasingly used in non-destructive testing of crop pests and diseases. This article first briefly introduced the basic principles of the new non-destructive testing technology represented by near-infrared thermal imaging technology and hyperspectral imaging technology and the image recognition technology represented by deep learning, and then systematically elaborated new imaging technologies and advanced image recognition and analysis technologies. The domestic and foreign research status in crop disease detection and identification was demonstrated, and its advantages and disadvantages in disease detection and identification were analyzed, with the advantages of rapidity and high accuracy, but the disadvantage of too large data volume to handle. The research trends and development directions of non-destructive testing of crop diseases were further pointed out, indicating that the combination of hyperspectral imaging with thermal infrared imaging and deep learning will be the development direction for the early detection of crop pests and diseases.

Key words: non-destructive testing, deep learning, hyperspectral imaging technology, image processing technology

CLC Number:

S123

WANG Yanxiang, ZHANG Yan, YANG Chengya, MENG Qinglong, SHANG Jing. Advances in new nondestructive detection and identification techniques of crop diseases based on deep learning[J]. , 2019, 31(4): 669-676.

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Advances in new nondestructive detection and identification techniques of crop diseases based on deep learning

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