## 1 Introduction  
In recent years, with the continuous development of the social economy, the demand for so-called \"vegetable basket\" projects has increased. The vegetable industry is developing rapidly in China, which currently has a greenhouse cultivation area measuring more than 4 million hectares--the largest in the world in terms of total area [1,2]. The temperature and humidity in the greenhouse environment are conducive to the proliferation and spread of airborne diseases (tomato gray mold (_Botrytis cinerea_), cucumber downy mildew (_Pseudoperonospora cubensis_), cucumber powdery mildew (_Podosphaera xanthii_), etc. ), which will increase with further expansion of the cultivation area and increases to the continuous planting period. In severe cases, production can be reduced, leading to a yield loss of 20-50% or even no harvest at all [3; 4; 5]. Therefore, an increasing need for accurate and real-time monitoring of airborne disease spores from greenhouse crops has emerged. With the improvement and popularization of spore trap devices, airborne fungal spores are commonly being collected by these devices. Previous studies have used them for sampling spores on microscope slides or plastic tape, which were coated with a thin film of petroleum jelly and then examined microscopically at one-hundred times magnification [6; 7]. The traditional microscopic spore counting method mainly relies on naked eye observation and classification by professional and technical personnel in a laboratory. Because of the great number of trapped spores, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, the timely identification and counting of trapped spores is hindered [8; 9]. In addition, polymerase chain reaction (PCR) testing has been extensively used in the detection of crop pathogenic fungi. However, it is difficult to translate these techniques to practical applications because of the high technical requirements and great operational complexity involved [10; 11]. Machine vision techniques have been widely used for the automatic diagnosis and grading of plant diseases in recent years, providing a basis for the intelligent and automated detection of crop diseases [12; 13]. The image recognition method, which is based on morphological features and machine learning, can complete the identification while capturing a large number of microparticle images. This method usually uses image acquisition devices to collect samples and then to analyze the morphological features to establish a library, then finally classifies them to finish the detection [14; 15; 16]. Recently, some studies on fungal spores using micro-image processing have been reported [17; 18]. [PERSON] et al. [19], in order to achieve quantitative monitoring of airborne spores, proposed an algorithm for the automatic detection and counting of spores based on digital image processing using the K-means clustering algorithm, image preprocessing, the identification of touching spores based on their shape factor and area, and touching spore contour segmentation based on concavity and contour segment merging. [PERSON] et al. [20] proposed a method based on image processing and an artificial neural network to automatically detect _P. xanthii_ spores. The correct identification rate of 63.6% when testing the pictures showed that it could automatically detect and count _P. xanthii_ spores. [PERSON] et al. [21] proposed a rapid rice blast detection and identification method based on the diffraction fingerprint textures of crop disease spores. This method can enrich and detect spores and allow early monitoring of crop diseases. Although the above research methods have achieved certain results, these results have only been for one type of spore. The diseases in greenhouses are complex and the morphologies are similar for some spores. For example, _B. cinerea_ spores are almost oval, _P. cubensis_ spores are lemon shaped, and _P. xanthii_ spores are cylindrical.