Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter’s direction was set to π/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse.

This study addresses the challenge posed by the small spore size of tomato gray mold, which hinders its identification and enumeration by conventional techniques. This work presents a novel approach for quantifying spore counts of tomato gray mold using diffraction imaging technology and image processing techniques. To construct a device for acquiring diffraction images of tomato gray mold spores, initially, the hyperspectral data pertaining to the gray mold spores of tomatoes was obtained. The characteristic wavelength of the light source of the diffraction image acquisition device was obtained by smoothing, principal component analysis, and comprehensive coefficient weight calculation. Then, the key parameters of the system were simulated, and the diffraction image acquisition device was built. Finally, tomato gray mold spores were counted based on angular spectrum reconstruction and image processing. The findings indicated that the combined contribution rate of the initial and secondary principal components of the original spectral data obtained from tomato gray mold spore samples amounted to 92.271%. The visible range of 435 nm, 475 nm, and 720 nm can be selected as the light source for tomato gray mold’s spore diffraction imaging system. CMOS image sensor was installed 45 mm below the micropore with a diameter of 100 μm, and the diffraction image obtained by simulation has a clear diffraction fingerprint. The diffraction imaging system can collect diffraction images of disease spores, and the collected diffraction images have clear diffraction fingerprints. The experimental error range was 5.13%-8.57%, and the average error was 6.42%. The error was within a 95% consistency. Therefore, this study can provide a research basis for the classification and recognition of greenhouse disease spores.