A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture.

BACKGROUND: Rice blast fungus is a worldwide disease, and it is one of the most serious rice diseases in the north and south rice fields in China. The initial symptoms of rice blast are not obvious, and the speed of transmission is fast. Manual identification is time-consuming and laborious. At present, it is a great challenge to realize rapid and accurate early identification of rice blast. RESULTS: In this paper, an identification method based on crop disease spores' diffraction fingerprint texture for rice blast was studied; this method utilizes the light field and texture features of diffraction images. To verify the reliability of the model that we proposed, we selected two methods of manual identification and machine recognition to compare and detect rice blast spores. The experimental results show that the identification of light diffraction characteristics is not only higher than the traditional manual recognition by microscope (increased by more than 0.3%), but also faster after neural network training (increased by more than 90%). The diffraction recognition method used in this study, based on crop disease spores' diffraction fingerprint texture, can be completed in a few seconds, and its test accuracy is 97.18%. CONCLUSION: The proposed method, a rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture, has certain advantages compared with the existing manual identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. © 2020 Society of Chemical Industry.

High-precision extraction and concentration detection of airborne disease microorganisms based on microfluidic chip.

Accurate monitoring of the content of specific disease micro-organisms in the air is one of the key technologies for early warning of airborne diseases. Based on the principle of aerosol particle motion in the microenvironment, this paper proposes a microfluidic chip method for accurately extracting diseased micro-organisms directly from the gas stream. The chip consists of a two-stage coupling of parallel double-sheath flow focusing and radial sheath flow acceleration. Considering the case of extracting mold spores (near spherical shape, average particle size 6 µ m) and strawberry gray mold spores (near spherical shape, average particle size 10 µ m) from the mixture (concentration of the mixture is about 3.4 × 10 8 /ml), the performance of the chip was evaluated using two indicators: extraction rate and purity. The results showed that the extraction rates of mold spores and gray mildew spores were 89% and 76% and the purges were 98% and 87%, respectively, achieving high-purity and accurate extraction of fungal spores and greatly improving the detection accuracy. It could be used as the development basis of microbial sensor for the early rapid detection of crop fungal diseases.