1. Introduction
The rapid growth of the world’s population continues to pose a challenge to food security. This problem is intensified by the inability to expand arable land, even though most of the world’s food supply comes from crop production. An important part of food security involves the protection of crops against disease to increase yields. Wheat is among the most important food crops in the world since cereal crops occupy the largest sown areas among all agricultural crops. Leaf diseases caused by fungal pathogens significantly reduce wheat yields and may be responsible for 15–20% of losses [
1,
2]. Moreover, wheat yield losses in South Russia reached 30–40% when conditions were favorable for fungal pathogens [
3,
4]. These are significant additional losses since the Russian Federation is the third-largest producer and the first-largest exporter of wheat in the last decade (
www.fao.org, accessed on 12 June 2024).
Early detection and precise identification of diseases are crucial challenges in wheat protection. The traditional method of disease detection and identification based on visual inspection is often not reliable enough and could lead to a significant decrease in agricultural production since the corresponding procedure cannot guarantee the required estimation accuracy. Moreover, it is also quite labor-intensive and demands intensive academic phytopathological training for farmers or agronomists. Thus, the implementation of artificial intelligence in agriculture automation is now being widely addressed [
5,
6].
The effective monitoring of plant diseases can be achieved through the utilization of specially designed devices. A variety of optical sensors are employed in fieldwork to collect data for the early detection of alteration in plant physiology, such as multispectral and hyperspectral reflectance sensors [
7,
8], thermal sensors [
9], and other types of sensors detailed in [
10]. Nevertheless, it is preferable to identify infectious diseases at the earliest possible stage, prior to the emergence of visible symptoms. One of the most promising areas of phytosanitary monitoring involves the use of samplers for the detection of pathogen spores in the atmosphere [
11]. The combination of this technology with novel diagnostics methodologies will facilitate a more expeditious and precise quantitative evaluation of airborne infection. The results may be further interpreted, particularly to detect primary inoculum thresholds, in order to predict disease development and, thereby, enhance the efficiency of crop protection.
So far, a variety of sampler designs have been proposed, including volumetric samplers based on suction, passive samplers relying on passive deposition [
11,
12], and microfluidic devices featuring low-pressure collection chambers [
13]. On the other hand, various methods are being developed to analyze the collected samples and to identify and quantify collected biological particles: immunological methods (e.g., ELISA), DNA-based diagnostics (e.g., PCR, TwistDX, and LAMP) and biosensors [
12,
14]. Among the various techniques employed for the analysis of spore samples, light microscopy is a widely used method, facilitating the identification of spore species and the enumeration of the detected pathogen spores [
15,
16,
17]. The extensive utilization of light microscopy can be attributed to its cost-effectiveness when compared to alternative methodologies. Given that the number of spores collected by samplers can exceed 1000 per sample, identifying and counting these numerous phytopathogenic spores is a meticulous and time-consuming process that requires the expertise of trained experts. Therefore, it is necessary to develop cost-effective and precise automated techniques that combine rapid sample processing with high-throughput identification of spore species.
The advent of sophisticated digital image processing technologies has given rise to the development of novel algorithms for the automatic detection and counting of spores. Some existing methods employ a series of image processing techniques for the automatic identification of objects in microscopic images of slides [
16,
18,
19]. In some instances, the extracted geometric features are employed to build object classifiers in images using machine learning methods [
20,
21,
22]. The advancement of deep learning techniques, particularly convolutional neural networks (CNNs) in computer vision, enables the automatic extraction of object features for the detection of diverse objects within images [
17,
23,
24,
25,
26,
27]. It is important to note that the majority of the studies mentioned above were conducted in laboratory settings. In contrast, using data collected under natural conditions, where images contain multiple fungal spores and other microparticles, results in complex backgrounds. This complexity invariably degrades the recognition quality of the target spores.
An advanced approach to the automatic evaluation of microscopic slides with a variety of spore types on a complex background is proposed in the present paper. The developed smart microscopy scanning system is based on the employment of an X-Y coordinate table for slide movement and convolutional neural networks (CNNs) for spore detection. The main objective of this study is to develop a universal approach to estimating the level of spore concentration on microscopic slides. The main novelty of the paper is the proposed two-stage algorithm for the automatic scanning of microscopic slides obtained from spore-trapping devices and estimating spore concentration. Moreover, we propose a methodology for calculating a universal measure of spore concentration, namely, a spore concentration density instead of calculating the number of spores. The efficacy of this methodology has been validated through experimentation with various exposure times. To the best of our knowledge, this study is the first to propose such a methodology.
To implement the intelligent system, namely, a smart microscopy scanning system, for the rapid and precise detection of spores, thereby enabling the automated processing of microscopic slides several stages have been passed. Initially, microscopic slides were exposed in a spore trapping device (as described in
Section 2.1) to create a data collection of images of several kinds of airborne spores (dataset) as described in
Section 2.3.1. Next, a smart device and the corresponding software based on a novel two-stage algorithm for scanning microscopic slides was developed, see
Section 2.2. The developed smart microscopy scanning system is to automatically process the entire microscope slide with multiple exposed strips that are automatically determined, and then identify and count the number of spores in each exposed strip. Accordingly, two CNNs were trained on the prepared dataset to enable their application for spore detection in micrographs. The selected CNNs, which have proven highly effective in object detection, are YOLOv8 and RT-DETR. The accuracy of these CNNs and the proposed algorithm for spore detection is demonstrated in
Section 3. Finally, the average spore concentration density for various exposure times is investigated.
4. Discussion
The experimental verification of the developed algorithm for automatic spore counting has demonstrated its practical applicability to the processing of microscopic slides with good quality. The two CNNs, YOLOv8 and RT-DETR, exhibited comparable performance in spotting the exposed strips. Manual counting typically considers wider strips than those analyzed by the proposed algorithm, but a unifying measure such as the proposed average spore concentration density—which counts the number of specific spores per square millimeter per minute—appears to be more accurate. Although the CNNs were trained on the yellow rust dataset, the algorithm confidently detects brown rust spores. It should be noted that the simultaneous and separate detection of brown and yellow rust spores, which appear similar to CNNs, requires training with a larger dataset than the one used in this study.
The notable discrepancy in spore counts implemented with the algorithm and manually is attributed to the degradation of spore recognition in some blurred images. The latter is caused by the non-uniform distribution of the glue on the microscopic slide, which results in changes in the distance to the microscope and its de-focusing. Nevertheless, these discrepancies are not critical, and the results produced by the smart microscopy scanning system allow us to adequately determine the number of spores in the air.
5. Conclusions
In the present study, an advanced smart microscopy scanning system for the rapid and precise identification of spores providing automated processing of microscopic slides is presented. It is shown that the average spore concentration density can be employed as a measure allowing for the standardization of results obtained under different conditions.
The proposed system is based on a novel two-stage algorithm, which has the potential to be extended to the detection of other types of spores. The presented methodology and the developed software can be adapted for the scanning and detection of various objects situated on microscopic slides. Furthermore, the method provides an effective tool for related researchers to fasten the processing of the microscopic slides for the collection of data on the changes in various agrocenosis, where the knowledge of the spore density is important for decision-making.
It should also be noted that the procedure used at the second stage of the proposed algorithm can be directly applied to the selected area of the slide using the developed software and the system. This enables the application of this specific stage of the algorithm not only to slides but also, for instance, to the enumeration of spores obtained using other types of spore-trapping devices. Although winter soft wheat (Triticum aestivum L.) was used to collect spores of phytopathogens due to its varying sensitivity to them, the proposed smart system can be readily extended to other grain crops, including soft spring wheat, and both winter and spring durum wheat (Triticum durum).