Detection and distribution of viruses infecting hot pepper (Capsicum spp.) in Rwanda

Waweru, Bancy; Miano, Douglas Watuku; Kilalo, Dora; Rukundo, Placide; Kimenju, J. W.

doi:10.1007/s42161-021-00811-7

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…Pepper is an important species, fully grown in most parts of Ethiopia, and its production could play a major role in accomplishing the country’s agricultural objective of food sufficiency. Pepper diseases cause reduction in the crops growth and financial losses, as well as a reduction in the quality and quantity of agricultural products 2 . Some of the types of pepper disease are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Bezabh,

Salau,

Abuhayi

et al. 2023

Sci Rep

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Agricultural products are vital to the sustainability of the economies of developing countries. Most developing countries’ economies such as Ethiopia heavily rely on agriculture. On a global scale, the pepper crop is one of the most important agricultural products in terms of human food security. However, it is susceptible to a variety of diseases which include blight leaf disease, gray leaf spot, common rust, fruit rot disease, powdery mildew symptoms on pepper leaf, and other related diseases that are all common today. Currently, more than 34 different pepper diseases have been discovered, resulting in a 33% average yield loss in pepper cultivation. Conventionally, farmers detect the disease using visual observation but this has its own demerits as it is usually not accurate and usually time consuming. In the past, a number of researchers have presented various methods for classifying pepper plant disease, especially using image processing and deep learning techniques. However, earlier studies have shown that binary classification requires improvement as some classes were more challenging to identify than others. In this study, we propose a concatenated neural network of the extracted features of VGG16 and AlexNet networks to develop a pepper disease classification model using fully connected layers. The development of the proposed concatenated CNN model includes steps such as dataset collection, image preprocessing, noise removal, segmentation, feature extraction, and classification. Finally, the proposed concatenated CNN model was evaluated, providing a training classification accuracy of 100%, validation accuracy of 97.29%, and testing accuracy of 95.82%. In general, it can be concluded from the findings of the study that the proposed concatenated model is suitable for identifying pepper leaf and fruit diseases from digital images of pepper.

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Section: Introductionmentioning

confidence: 99%

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Bezabh,

Salau,

Abuhayi

et al. 2023

Sci Rep

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“…Other symptoms include the crop exhibiting some degree of leaf withering. The fruit has the potential to be undersized and misshapen [28].…”

Section: Virus-caused Chili Diseasementioning

confidence: 99%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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Chili, an important crop whosefruit is used as a spice, is significantly hampered by the existence of chili diseases.While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 researchpaperpublications on theidentificationof chilidiseases, with publication dates spanning from 2013 to 2021, arereviewed in this paper.Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.

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“…A wide range of phytopathogens, including fungi (such as Colletotrichum truncatum, Phytopthora capsici, and Leveillula taurica), bacteria (Xanthomonas and Ralstonia), viruses (such as tobamoviruses, tospoviruses, potyviruses, and cucumoviruses), nematodes, and insects, damage capsicum plants, resulting in extensive yield losses (6). Of all the categories of phytopathogens, viruses are the most destructive biotic agents in capsicum in particular (7,8). At present, 68 virus species have been reported in capsicum.…”

Section: Introductionmentioning

confidence: 99%

“…At present, 68 virus species have been reported in capsicum. Among these, 45 species belonging to eight genera, viz., Alfamovirus, Begomovirus, Cucumovirus, Potyvirus, Polerovirus, Potexvirus, Tobamovirus, and Tospovirus, cause considerable economic loss in capsicum production worldwide (7). Among all, tobamoviruses (family Virgaviridae) cause severe losses to the crop and are one of the most studied plant viruses (7,9).…”

Section: Introductionmentioning

confidence: 99%

“…In capsicum, tobamoviruses are one of the most damaging viruses, particularly in protected cultivation. Tobacco mosaic virus (TMV), pepper mild mottle virus (PMMoV), tomato mosaic virus (ToMV), tobacco mild green mosaic virus (TMGMV), bell pepper mottle virus (BPeMV), paprika mild mottle virus (PaMMV), and Obuda pepper virus (OBPV) are the most widespread tobamoviruses infecting capsicum worldwide (7). PMMoV was initially recognized in the United States as a latent strain of TMV and named as South Carolina mild mottling strain of TMV.…”

Section: Introductionmentioning

confidence: 99%

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Pepper mild mottle virus: a formidable foe of capsicum production—a review

Kumari,

Sharma,

Patel

et al. 2023

Front. Virol.

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Viruses are one of the major restraining factors in pepper cultivation globally. Among different viruses, pepper mild mottle virus (PMMoV) is one of the most detrimental plant viruses infecting Capsicum spp. belonging to the genus Tobamovirus and Virgaviridae family. It has a monopartite positive-sense single-stranded RNA genome of 6.35 kb size. On an average, PMMoV results in 15%–40% losses in capsicum fruit yield. However, the incidence of PMMoV can reach as high as 95%, leading to substantial yield losses ranging from 75% to 95%. The virus is transmitted via contact, soil, and seeds rather than via insect vectors. PMMoV, because of its seed-borne nature, now occurs worldwide. PMMoV mainly infects Capsicum spp. under natural conditions; however, it can experimentally be transmitted to other plants species belonging to the families Solanaceae, Cucurbitaceae, Labiatae, Chenopodiaceae, and Plantaginaceae. The resistance to tobamoviruses in capsicum is conferred by L locus. Mutations in the coat protein of PMMoV are responsible for the emergence of L-mediated resistance-breaking pathotypes. The highly contagious nature of the virus, seed transmission behavior, and the emergence of virulence complicate its management through a single approach. Therefore, efforts are directed towards providing a more practical and efficient integrated management solution using the RNA interference approach; exploitation of the L gene for resistance breeding; and the inhibitory potential of natural products, systemic resistance-inducing antagonistic bacteria, and chemically synthesized silver nanoparticles. Markers linked to L alleles have been observed to accelerate capsicum breeding programs through marker-assisted selection. In this study, an attempt has been made to compile the recent developments in PMMoV biology, pathogenic variability, genomic organization, and management strategies.

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Detection and distribution of viruses infecting hot pepper (Capsicum spp.) in Rwanda

Cited by 9 publications

References 30 publications

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Pepper mild mottle virus: a formidable foe of capsicum production—a review

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