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Rapid Crop Authenticity Verification Aided by Algorithm Advancement
News

Rapid Crop Authenticity Verification Aided by Algorithm Advancement

Rapid Crop Authenticity Verification Aided by Algorithm Advancement
News

Rapid Crop Authenticity Verification Aided by Algorithm Advancement

Heat maps of the confusion matrices of wheat (a) and rice (b) sample sets identified by InResSpectra. Credit: XU Zhuopin.
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Recently, the crop quality intelligent perception team of Hefei Institute of Intelligent Machinery, Hefei Institutes of Physical Science (HFIPS) of Chinese Academy of Sciences (CAS) has developed a new algorithm in the direction of near-infrared spectroscopy, which is suitable for high-throughput identification of the authenticity of crop varieties.


The related work was published in Infrared Physics & Technology.


The authenticity of crop varieties is of great significance in variety protection and seed breeding. Traditional methods for authentic identification of crop varieties, such as DNA molecular identification, isoenzyme identification, and field identification, have the disadvantages of complicated operations, time-consuming, samples damage, environmental pollution, and lagging detection results, so an effective method is urgently needed to realize the authenticity identification of crop varieties.


As a rapid detection technology, near-infrared spectroscopy (NIRS), has many advantages. It's environmental-friendly, highly-sensitive, and non-destructive.


In this research, the self-developed high-throughput seed quality sorting instrument based on near-infrared spectroscopy, which was made by team, can achieve rapid identification and sorting of individual seeds. Based on this instrument, researchers proposed an improved convolution neural network (CNN)——the InResSpectra network, to help achieve more accurate seed variety identification. This was an optimized Inception network. It successfully removed the 1 × 1 convolution branch to reduce the complexity of the model, and increased the residual element of the ResNet network, which accelerated the training of the neural network and improved accuracy.


Researchers applied the developed system and the InResSpectra network for the identification of 24 wheat varieties and 21 rice varieties, and achieved 95.35% and 93.07% accuracy, respectively, which provided an effective method for the spectroscopic identification of the authenticity of crop varieties.


Reference: Li X, Xu Z, Cheng W, et al. Research on high-throughput crop authenticity identification method based on near-infrared spectroscopy and InResSpectra model. Infrared Phys Tech. 2022;125:104235. doi:10.1016/j.infrared.2022.104235


This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.


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