1
|
Sharma G, Dwibedi V, Seth CS, Singh S, Ramamurthy PC, Bhadrecha P, Singh J. Direct and indirect technical guide for the early detection and management of fungal plant diseases. CURRENT RESEARCH IN MICROBIAL SCIENCES 2024; 7:100276. [PMID: 39345949 PMCID: PMC11428012 DOI: 10.1016/j.crmicr.2024.100276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024] Open
Abstract
Fungal plant diseases are a major threat to plants and vegetation worldwide. Recent technological advancements in biotechnological tools and techniques have made it possible to identify and manage fungal plant diseases at an early stage. These techniques include direct methods, such as ELISA, immunofluorescence, PCR, flow cytometry, and in-situ hybridization, as well as indirect methods, such as fluorescence imaging, hyperspectral techniques, thermography, biosensors, nanotechnology, and nano-enthused biosensors. Early detection of fungal plant diseases can help to prevent major losses to plantations. This is because early detection allows for the implementation of control measures, such as the use of fungicides or resistant varieties. Early detection can also help to minimize the spread of the disease to other plants. The techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases. These techniques can help to ensure food security and protect our valuable plant resources.
Collapse
Affiliation(s)
- Gargi Sharma
- Department of Biotechnology, University Institute of Biotechnology, Chandigarh University, Gharuan, 140413, Punjab, India
| | - Vagish Dwibedi
- Department of Biotechnology, University Institute of Biotechnology, Chandigarh University, Gharuan, 140413, Punjab, India
- Agriculture Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
| | | | - Simranjeet Singh
- Interdisciplinary Centre for Water Research (ICWaR), Indian Institute of Science, Bengaluru, Karnataka, 560012
| | - Praveen C Ramamurthy
- Interdisciplinary Centre for Water Research (ICWaR), Indian Institute of Science, Bengaluru, Karnataka, 560012
| | - Pooja Bhadrecha
- Department of Biotechnology, University Institute of Biotechnology, Chandigarh University, Gharuan, 140413, Punjab, India
| | - Joginder Singh
- Department of Botany, Nagaland University, Lumami, Nagaland, India
| |
Collapse
|
2
|
Tomaszewski M, Nalepa J, Moliszewska E, Ruszczak B, Smykała K. Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning. Sci Rep 2023; 13:7671. [PMID: 37169807 PMCID: PMC10175501 DOI: 10.1038/s41598-023-34079-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.
Collapse
Affiliation(s)
- Michał Tomaszewski
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland.
| | - Jakub Nalepa
- Department of Algorithmics and Software, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
- KP Labs, Konarskiego 18C, 44-100, Gliwice, Poland
| | - Ewa Moliszewska
- Faculty of Natural Sciences and Technology, Institute of Environmental Engineering and Biotechnology, University of Opole, Ks. B. Kominka 6a Street, 45-032, Opole, Poland
| | - Bogdan Ruszczak
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland
- KP Labs, Konarskiego 18C, 44-100, Gliwice, Poland
| | - Krzysztof Smykała
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland
- QZ Solutions Sp. z o.o., Ozimska 72A Street, 45-310, Opole, Poland
| |
Collapse
|
3
|
Pasin F. Assembly of plant virus agroinfectious clones using biological material or DNA synthesis. STAR Protoc 2022; 3:101716. [PMID: 36149792 PMCID: PMC9519601 DOI: 10.1016/j.xpro.2022.101716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/29/2022] [Accepted: 08/26/2022] [Indexed: 01/26/2023] Open
Abstract
Infectious clone technology is universally applied for biological characterization and engineering of viruses. This protocol describes procedures that implement synthetic biology advances for streamlined assembly of virus infectious clones. Here, I detail homology-based cloning using biological material, as well as SynViP assembly using type IIS restriction enzymes and chemically synthesized DNA fragments. The assembled virus clones are based on compact T-DNA binary vectors of the pLX series and are delivered to host plants by Agrobacterium-mediated inoculation. For complete details on the use and execution of this protocol, please refer to Pasin et al. (2017, 2018) and Pasin (2021).
Collapse
Affiliation(s)
- Fabio Pasin
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas - Universitat Politècnica de València (CSIC-UPV), 46011 Valencia, Spain.
| |
Collapse
|