Cassava diseases caused byXanthomonas phaseolipv.manihotisandXanthomonas cassavae

Zárate-Chaves, Carlos A; Cruz, Diana Gómez de la; López, Camilo; Bernal, Adriana; Szurek, Boris

doi:10.1111/mpp.13094

Cited by 30 publications

(12 citation statements)

References 152 publications

(334 reference statements)

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“…Water-soaked lesions spread and increased in visibility at 6 and 9 DPI. However, as previously reported [33] water-soaking appeared reduced in Xam668ΔTAL20 infection sites as compared to wildtype Xam668 sites. Additionally, Xam668ΔTAL20 infection sites appeared lighter in color compared to the darker lesions that develop at wildtype Xam668 sites.…”

Section: Xam Induction Of Water-soaked Lesions In Cassavasupporting

confidence: 83%

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A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity

Elliott

Berry

Kim

et al. 2022

Preprint

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BackgroundMethods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results.ResultsIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types.ConclusionsBoth image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.

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Section: Xam Induction Of Water-soaked Lesions In Cassavasupporting

confidence: 83%

“…Cassava production is threatened by the disease cassava bacterial blight (CBB). CBB can result in complete crop loss and is present in all cassava growing regions (Howler 2013 [31], Fanuo 2017 [32], Zárate-Chaves 2021 [33]). The causal agent of CBB is Xanthomonas axonopodis pv.…”

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confidence: 99%

A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity

Elliott

Berry

Kim

et al. 2022

Preprint

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“…An outbreak of cassava diseases and pests, which impacts cassava yields and plant security, is one of the fundamental causes contributing to low output [4]. Some of these diseases include cassava brown streak virus disease [5], cassava bacterial blight [6], cassava green mite [7] and cassava mosaic disease [8]. To deal with the effects of these diseases on the Cassava plant, accurate and timely detection of these diseases is so vital.…”

Section: Introductionmentioning

confidence: 99%

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

Okokpujie¹,

Okokpujie²,

Ayomikun³

et al. 2023

MMEP

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Cassava is one of the six food items identified as a critical food product for Africa, owing to its importance to African farmers' lives and ability to alter African economies. However, Cassava plant diseases have affected the yield of farmers significantly which has led to a decline in the agricultural production of cassava. Therefore, the aim of this research work is to develop a web and mobile applications-based system that would be able to detect cassava diseases based on its leaf images. To achieve this aim, pre-trained Convolutional Neural Network (CNN) models were selected using their previous performance and the application of transfer learning technique, new models were developed to classify cassava diseases based on the dataset curated and pre-processed. The best three models were selected: MobileNetV2, VGG16 and ResNet50. After training, the accuracy for each model was: 98%, 92% and 75% for MobileNetV2, VGG16 and ResNet50 respectively. Following evaluation of performance, the model with the best accuracy (MobileNetV2) was deployed using a web application interface. After deploying as a web and mobile apps interface, it was further tested to see how it would perform on the field. This research work was found capable of aiding farmers in being able to timely detect the type of disease affecting their cassava plants and the correct treatment to utilize; this also contribute towards Sustainable development goals (SDG) 2 and 12.

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“…manihotis = X. phaseoli pv. manihotis) (Bredeson et al, 2016;Constantin et al, 2016;Zárate-Chaves et al, 2021), cassava brown streak disease (Cassava brown streak virus, CBSV) and anthracnose disease (Colletotrichum gloeosporioides) (Utsumi et al, 2016). Therefore, it is best to identify the NBS-LRR proteins in cassava.…”

Section: Introductionmentioning

confidence: 99%

The Cassava NBS-LRR Genes Confer Resistance to Cassava Bacterial Blight

Zhang

Liu

et al. 2022

Front. Plant Sci.

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Cassava bacterial blight (CBB) caused by Xanthomonas axonopodis pv. manihotis (Xam) seriously affects cassava yield. Genes encoding nucleotide-binding site (NBS) and leucine-rich repeat (LRR) domains are among the most important disease resistance genes in plants that are specifically involved in the response to diverse pathogens. However, the in vivo roles of NBS-LRR remain unclear in cassava (Manihot esculenta). In this study, we isolated four MeLRR genes and assessed their expression under salicylic acid (SA) treatment and Xam inoculation. Four MeLRR genes positively regulate cassava disease general resistance against Xam via virus-induced gene silencing (VIGS) and transient overexpression. During cassava-Xam interaction, MeLRRs positively regulated endogenous SA and reactive oxygen species (ROS) accumulation and pathogenesis-related gene 1 (PR1) transcripts. Additionally, we revealed that MeLRRs positively regulated disease resistance in Arabidopsis. These pathogenic microorganisms include Pseudomonas syringae pv. tomato, Alternaria brassicicola, and Botrytis cinerea. Our findings shed light on the molecular mechanism underlying the regulation of cassava resistance against Xam inoculation.

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Cassava diseases caused byXanthomonas phaseolipv.manihotisandXanthomonas cassavae

Cited by 30 publications

References 152 publications

A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity

A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

The Cassava NBS-LRR Genes Confer Resistance to Cassava Bacterial Blight

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