Plant pathogenicity and associated/related detection systems. A review
Graphical abstract
Introduction
Pathogens are microorganisms (bacteria, fungus, viruses, etc.) that cause diseases. Pathogens in the agricultural sector have a negative effect on food quality and safety. The yield loss range at a global level for five major crops are: wheat 10.1–28.1%, rice 24.6–40.9%, maize 19.5–41.1%, potato 8.1–21.0% and soybean 11.0–32.4% ranges that crop pathogens and pests reduce the yield and quality of agricultural production [1]. The various plant pathogens arising from different sources along with types of infection caused by them have been listed in Table 1. The pathogens listed in the table are examples of most prevalent disease-causing causing microbes which are categorized based on their source of infection into soil, airborne air borne. Soil serves as an important habitat for the growth of soil borne pathogens wherein extrinsic as well as intrinsic parameters play an important role such as soil pH, soil biota, temperature, moisture, and organic contaminants. Most soil borne pathogens rot the underground tissues and vascular wilts and a few of them are also known to cause foliar diseases. For example, Lettuce anthracnose infects the lettuce leaves as the fungus travels up to the leaves in the plant [2]. Water-borne pathogens are the disease causing agents which are being introduced into the agricultural lands by irrigation. Air-borne pathogens travel through air. In particular, the diseases which have attacked the leaves of plants spread by wind or droplets of rain or during irrigation [3]. More information on various sources of pathogens can be found in the literature [4,5]. There are different screening techniques have been developed over the years for various diseases caused by pathogens. From the simplest detection of symptoms appearing on leaves to more microscopic observation, the nucleic acid detection techniques as illustrated in Fig. 1.
The standard analytical techniques for detection of plant pathogens have been categorized into two types: The direct and the indirect methods of detection. The direct methods include: Polymerase chain reaction [6], immuno-assays (JR. 1995) and the culture colony counting. The indirect methods include thermography [7], hyper spectral imaging, gas chromatography and fluorescence imaging. To maximize productivity and minimize the agricultural losses due to pathogens, advanced plant disease detection and early screening serves to be an important aspect of plant sciences. There are various modes of infection spread which occur in agricultural lands. The different modes are through insects, wind, and water-soil transmissions as illustrated in Fig. 2. In last decade, various types of biosensors were reported for detecting plant diseases properties of those biosensors include their ease of use, high specificity, sensitivity, lower limits of detection, and multi-array capability. In this review, we present a thorough review covering the various types of plant pathogens, its sampling procedures and a detailed discussion on types of biosensors used for the detection of diseases on field.
Section snippets
Methods for plant disease detection
Various plant disease detection strategies have been implemented to detect the diseases and symptoms in plants to implement control strategies as early as possible. The traditional methods of plant disease detection are: visual/microscopic examination, polymerase chain reaction (PCR) and Enzyme Linked ImmunoSorbent Assays (ELISA). Bacteria, fungi, protists and other microbes can usually be observed using a light microscope. Microbiological characteristics can help us to identify the pathogen of
Biosensors for plant pathogen detection
Biosensors generally consist of a physicochemical transducer and a molecular recognition element, known as a receptor molecule which interacts with specific target analytes [37]. Upon interaction of receptors with the target analytes, a bio-recognition information is produced which is converted into an electrochemical, electrical, optical signal, etc. by the transducer. Fig. 4. Illustrates the principle of biosensor with different biorecognition elements used. Khater et al. has provided a
Conclusion and future outlook
Early detection of plant disease is a crucial strategy to minimize the losses. As mentioned in this review, indirect methods have shown that these techniques can be efficient in early detection of symptoms, but also in cases where plants do not yet show symptoms. Implementation of such methods in large scale applications such as in identification of early stages of infections in large canopies and farms, have helped in implementing controls strategies in earliest possible stages to reduce crop
Funding
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This work was financially supported by the grant from India–Trento Program for Advanced Research (ITPAR – IV) Department of Science and Technology (DST) Government of India, in the field of agricultural research.
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DST, Government of India for the INSPIRE Fellowship to the first author
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European Union's Horizon 2020 research and innovation program for the Marie Skłodowska-Curie grant agreement No 813680 (AQUASENSE)
Ethics approval/declarations
This is not applicable.
Consent to participate
This paper does not involve humans or animals, therefore Ethical approval consent is not applicable.
Consent for publication
This study doesn't contain any data from any individual person, therefore consent for publication is not applicable.
Code availability
Not applicable for this study.
Authors contribution
Rhea Patel and Bappa Mitra did the literature search, data analysis, and drafted the manuscript.; Madhuri Vinchurkar, Rajul Patkar, Andrea Adami, Flavio Giacomozzi, Leandro Lorenzelli, Maryam Shojaei Baghini critically revised the work.; All authors conceptualized the review paper and outline was made based on every author's inputs and experience.; All authors commented on previous versions of the manuscript.; All authors read and approved the final manuscript.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bappa Mitra reports financial support was provided by European Union's Horizon 2020. Rhea Patel reports financial support was provided by INSPIRE, DST, India. Rhea Patel, Madhuri Vinchurkar, Rajul Patkar, Maryam Shojaei Baghini, Andrea Adami, Flavio Giacomozzi, Leandro Lorenzelli reports financial support was provided by Department of Science and Technology,
Acknowledgment
This work was supported by the grant from India–Trento Program for Advanced Research (ITPAR – IV) Department of Science and Technology (DST) Government of India, in the field of agricultural research. Authors would also like to acknowledge DST, Government of India for the INSPIRE Fellowship to the first author and the European Union's Horizon 2020 research and innovation programme for the Marie Skłodowska-Curie grant agreement No 813680 (AQUASENSE).
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