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Abstract

Several species of Solanum produce secondary metabolites with antimicrobial activity. In

the present study, the inhibitory activity of Solanum chrysotrichum, S. erianthum, S. torvum

and S. rostratum against phytopathogenic Curvularia lunata was determined. Methanol extracts

from roots, stems, leaves and fruits were evaluated by the method of mycelial inhibition

on agar and the minimum inhibitory concentration (MIC) was determined on a liquid

medium. To increase the antimicrobial activity, the combined activity of the most active

extracts for each phytopathogen was also determined (a combination of intra and interspecies

extracts). The results showed that 12 of the 16 methanolic extracts of Solanum species

had antifungal effects against C. lunata. The extracts of S. rostratum and S. erianthum

developed the highest activity (~80% inhibition and 28.4 MIC μg . ml–1), even, equal to or

greater than, the reference fungicide. The mixture of the active extracts of S. chrysotrichum

and S. torvum increased their activity. Various extracts affected the macro and microscopic

morphology and most of them reduced the number of conidia of the fungus. This resulted

in the capacity to control the vegetative growth and reproduction of C. lunata, the causal

fungus of corn leaf spot disease.

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Authors and Affiliations

Zuleima Guadalupe Hernández-Rodríguez
Christian Anabi Riley-Saldaña
Alma Rosa González-Esquinca
Marisol Castro-Moreno
Iván de-la-Cruz-Chacón
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Abstract

Modern agriculture and plant breeding must continuously meet the high and increasingly growing requirements of consumers and recipients. In this context, one of the conditions for effective management of any farm is access to quick and efficient diagnostics of plant pathogens, the result of which, together with the assessment of experts, provide breeders with tools to effectively reduce the occurrence of plant diseases. This paper presents information about biodiversity and spectrum of endophytic and phytopathogenic bacterial species identified in plant samples delivered to the Plant Disease Clinic in 2013–2019. During the tests, using the Biolog Gen III system, the species affiliation of the majority of detected bacterial strains found in plant tissues as an endophyte and not causing disease symptoms on plants was determined. These data were compiled and compared with the number of found identifications for a given species and data on the pathogenicity of bacterial species towards plants. In this way, valuable information for the scientific community was obtained about the species composition of the bacterial microbiome of the crop plants studied by us, which were confronted with available literature data. In the study, special attention was paid to tomato, which is the plant most often supplied for testing in the Plant Disease Clinic due to its economic importance.
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Authors and Affiliations

Weronika Zenelt
1
Krzysztof Krawczyk
2
Natasza Borodynko-Filas
1
ORCID: ORCID

  1. Plant Disease Clinic and Bank of Plant Pathogen, Institute of Plant Protection – National Research Institute, Poznań, Poland
  2. Department of Molecular Biology and Biotechnology, Institute of Plant Protection – National Research Institute, Poznań, Poland
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Abstract

The world population is projected to reach 9.8 billion in 2050, and 11.2 billion in 2100 (United Nations) and people will need food, and decrease the farming land. Thus, the importance of Internet of Things (IoT) and Computer Science (CS) in plant disease management are increasing now-a-days. Mobile apps, remote sensing, spectral signature analysis, artificial neural networks (ANN), and deep learning monitors are commonly used in plant disease and pest management. IoT improves crop yield by fostering new farming methods along with the improvement of monitoring and management through cloud computing. In the quest for effective plant disease control, the future lies in cutting-edge technologies. The integration of IoT, artificial intelligence, and data analytics revolutionizes monitoring and diagnosis, enabling timely and precise interventions. Cloud computing facilitates real-time data sharing and analysis empower farmers to combat diseases with unprecedented efficiency. By harnessing these innovations, agriculture can embrace sustainable practices and safeguard crop health, ensuring a bountiful and secure future for the global food supply.
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Authors and Affiliations

Suborna Rani
1
Kallol Das
2
ORCID: ORCID
F.M. Aminuzzaman
3
ORCID: ORCID
Benjamin Yaw Ayim
4
ORCID: ORCID
Natasza Borodynko-Filas
5
ORCID: ORCID

  1. Faculty of Computer Science and Engineering, Patuakhali Science and Technology University, Patuakhali, Bangladesh
  2. College of Agriculture and Life Sciences, Kyungpook National University, Daegu, Republic of Korea
  3. Department of Plant Pathology, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh
  4. Ministry of Food and Agriculture, Plant Protection and Regulatory Services Directorate, Ashanti, Ghana
  5. Plant Disease Clinic and Bank of Pathogens, Institute of Plant Protection – National Research Institute, Poznan, Poland
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Abstract

Spectroscopy has become one of the most used non-invasive methods to detect plant diseases before symptoms are visible. In this study it was possible to characterize the spectral variation in leaves of Solanum lycopersicum L. infected with Fusarium oxysporum during the incubation period. It was also possible to identify the relevant specific wavelengths in the range of 380–1000 nm that can be used as spectral signatures for the detection and discrimination of vascular wilt in S. lycopersicum. It was observed that inoculated tomato plants increased their reflectance in the visible range (Vis) and decreased slowly in the near infrared range (NIR) measured during incubation, showing marked differences with plants subjected to water stress in the Vis/NIR. Additionally, three ranges were found in the spectrum related to infection by F. oxysporum (510–520 nm, 650–670 nm, 700–750 nm). Linear discriminant models on spectral reflectance data were able to differentiate between tomato varieties inoculated with F. oxysporum from healthy ones with accuracies higher than 70% 9 days after inoculation. The results showed the potential of reflectance spectroscopy to discriminate plants inoculated with F. oxysporum from healthy ones as well as those subjected to water stress in the incubation period of the disease.

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Authors and Affiliations

Juan Carlos Marín Ortiz
Lilliana María Hoyos Carvajal
Veronica Botero Fernandez
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Abstract

The goal of this study was to evaluate the effectiveness of aqueous extracts from five traditional Egyptian medicinal plants in preventing Sphaerotheca fuliginea’s powdery mildew disease, which affects cucumber plants. Aqueous extracts from each of the examined plants suppressed the pathogen’s conidia germination in vitro. In trials using detached leaves and greenhouses, these extracts lessened the severity of the disease. Compared to other plant extracts, Curcuma longa rhizome extract showed the greatest potency against the pathogen. The aqueous extract of Curcuma longa showed the largest improvement in disease suppression compared to the control in the greenhouse experiment. The results showed that total phenol and associated defense enzyme levels (POD and PPO) were elevated by plant extracts from all studied plants. These findings might suggest that total phenol and associated defense enzymes strengthen the cucumber’s resistance to the disease. The C. longa extract had more total phenol than the extracts from the other plants. The phenolic components in the C. longa rhizome extract were varied, and these variations were detected and quantified using high-performance liquid chromatography (HPLC). The content of curcumin (3220.8 μg · g –1 dry weight) was the highest. In comparison to the control, the foliar application of the C. longa extract considerably increased the cucumber fruit yield and its constituent parts. This is the first time, to my knowledge, that the C. longa rhizome extract has been utilized to improve cucumber plants’ production and its constituent parts. The pathogen appeared as small colonies with fewer mycelia and immature conidia in the treated cucumber leaves with 20% of C. longa rhizome extract according to an examination by SEM. Overall, the results indicated that the extract of C. longa rhizome, was a promising, effective, and environmentally friendly management measure against powdery mildew disease of cucumbers, and thus could be used in the production of organically grown vegetables.
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Authors and Affiliations

Zakaria Awad Baka
1

  1. Department of Botany and Microbiology, Faculty of Science, University of Damietta, New Damietta, Egypt
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Abstract

The field of plant pathology has adopted targeted genome editing technology as one of its most crucial and effective genetic tools. Due to its simplicity, effectiveness, versatility, CRISPR together with CRISPR-associated proteins found in an adaptive immune system of prokaryotes have recently attracted the interest of the scientific world. Plant disease resistance must be genetically improved for sustainable agriculture. Plant biology and biotechnology have been transformed by genome editing, which makes it possible to perform precise and targeted genome modifications. Editing offers a fresh approach by genetically enhancing plant disease resistance and quickening resistance through breeding. It is simpler to plan and implement, has a greater success rate, is more adaptable and less expensive than other genome editing methods. Importantly CRISPR/Cas9 has recently surpassed plant science as well as plant disease. After years of research, scientists are currently modifying and rewriting genomes to create crop plants which are immune to particular pests and diseases. The main topics of this review are current developments in plant protection using CRISPR/Cas9 technology in model plants and commodities in response to viral, fungal, and bacterial infections, as well as potential applications and difficulties of numerous promising CRISPR/Cas9-adapted approaches.
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Authors and Affiliations

Kallol Das
1 2
ORCID: ORCID
Benjamin Yaw Ayim
3
ORCID: ORCID
Natasza Borodynko-Filas
4
ORCID: ORCID
Srijan Chandra Das
5
F.M. Aminuzzaman
2
ORCID: ORCID

  1. College of Agriculture and Life Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
  2. Department of Plant Pathology, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka-1207, Bangladesh
  3. Ministry of Food and Agriculture, Plant Protection and Regulatory Services Directorate, Ashanti 23321, Ghana
  4. Plant Disease Clinic and Bank of Pathogens, Institute of Plant Protection – National Research Institute, Poznan, Poland
  5. Bangladesh Rice Research Institute, Rice Farming System Division, Regional Station, Gopalganj, Bangladesh
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Abstract

The tomato crop is more susceptible to disease than any other vegetable, and it can be infected with over 200 diseases caused by different pathogens worldwide. Tomato plant diseases have become a challenge to food security globally. Currently, diagnosing and preventing tomato plant diseases is a challenge due to the lack of essential methods or tools. The traditional techniques of detecting plant disease are arduous and error-prone. Utilizing precise or automatic detection methods in spotting early plant disease can improve the quality of food production and reduce adverse effects. Deep learning has significantly increased the recognition accuracy of image classification and object detection systems in recent years. In this study, a 15-layer convolutional neural network is proposed as the backbone for single shot detector (SSD) to improve the detection of healthy, and three classes of tomato fruit diseases. The proposed model performance is compared with ResNet-50, AlexNet, VGG 16, and VGG19 as the backbone for Single shot detector. The findings of the experiment showed that the proposed CNN-SDD achieved 98.87% higher detection accuracy, which outperformed state-of-the-art models.
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Authors and Affiliations

Benedicta Nana Esi Nyarko
1
ORCID: ORCID
Wu Bin
1
Zhou Jinzhi
1
ORCID: ORCID
Justice Odoom
1
ORCID: ORCID

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China
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Abstract

Plant viruses cause crop losses in agronomically and economically important crops, making global food security a challenge. Although traditional plant breeding has been effective in controlling plant viral diseases, it is unlikely to solve the problems associated with the frequent emergence of new and more virulent virus species or strains. As a result, there is an urgent need to develop alternative virus control strategies that can be used to more easily contain viral diseases. A better understanding of plant defence mechanisms will open up new avenues for research into plant- pathogen interactions and the development of broad-spectrum virus resistance.
The scientific literature was evaluated and structured in this review, and the results of the reliability of the methods of analysis used were filtered. As a result, we described the molecular mechanisms by which viruses interact with host plant cells.
To develop an effective strategy for the control of plant pathogens with a significant intensity on the agricultural market, clear and standardised recommendations are required. The current review will provide key insights into the molecular underpinnings underlying the coordination of plant disease resistance, such as main classes of resistance genes, RNA interference, and the RNA-mediated adaptive immune system of bacteria and archaea – clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated Cas proteins – CRISPR/Cas.
Future issues related to resistance to plant viral diseases will largely depend on integrated research to transfer fundamental knowledge to applied problems, bridging the gap between laboratory and field work.
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Authors and Affiliations

Nurgul Iksat
1
ORCID: ORCID
Zhaksylyk Masalimov
1
ORCID: ORCID
Rustem Omarov
1
ORCID: ORCID

  1. L.N. Gumilyov Eurasian National University, Faculty of Natural Sciences, Satbayeva St. 2, Astana 010000, Kazakhstan
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Abstract

Leaf - a significant part of the plant, produces food using the process called photosynthesis. Leaf disease can cause damage to the entire plant and eventually lowers crop production. Machine learning algorithm for classifying five types of diseases, such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew, Leaf Curl and Myrothecium leaf diseases, is proposed in the proposed study. The classification of diseases needs front face of leafs. This paper proposes an automated image acquisition process using a USB camera interfaced with Raspberry PI SoC. The image is transmitted to host PC for classification of diseases using online web server. Pre-processing of the acquired image by host PC to obtain full leaf, and later classification model based on SVM is used to detect type diseases. Results were checked with a 97% accuracy for the collection of acquired images.
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Authors and Affiliations

Hiren Mewada
1
Jignesh Patoliaya
2

  1. Faculty of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Kobhar, Kingdom of Saudi Arabai
  2. Charotar University of Science and Technology, Changa, India

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