Common potato disease symptoms: ambiguity of symptom-based identification of causal pathogens and value of on-site molecular diagnostics

Mora‐Romero, Guadalupe Arlene; Félix-Gastélum, Rubén; Bomberger, Rachel A.; Romero-Urías, Cecilia; Tanaka, Kiwamu

doi:10.1007/s10327-021-01045-2

Cited by 11 publications

(3 citation statements)

References 100 publications

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“…Eggplants are susceptible to many diseases including soilborne diseases causing great losses in yield and quality. In general, diseases caused by soilborne pathogens are difficult to control, and plant resistance to many soilborne pathogens has not been fully studied [1][2][3]. Plant-parasitic nematodes are also not an exception to soilborne pathogens, causing high losses in the agriculture sector for eggplants [4].…”

Section: Introductionmentioning

confidence: 99%

Purpureocillium lilacinum for plant growth promotion and biocontrol against root-knot nematodes infecting eggplant

Khan

Tanaka

2023

PLoS ONE

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Purpureocillium lilacinum is a biocontrol Ascomycota fungus against various plant pathogens. In the present study, the efficacy of P. lilacinum was evaluated against a root-knot nematode, Meloidogyne incognita that infects eggplants. We performed an in vitro experiment in which the direct effects of P. lilacinum on the second-stage juvenile survival and egg hatching of M. incognita were tested at different exposure times. The results showed that P. lilacinum significantly reduced the rates of egg hatching and juvenile survival in a dose-dependent manner. Microscopic observation demonstrated that P. lilacinum directly penetrated the eggs and contacted the juveniles, indicating how P. lilacinum parasitizes M. incognita. We also performed a pot assay in which soil-grown eggplants were treated with P. lilacinum followed by inoculation with M. incognita. The results indicated that P. lilacinum effectively reduced the nematode population and the number of galls in plant roots. Interestingly, application of P. lilacinum resulted in significant enhancements in plant growth and biomass, even under nematode infection, while it improved plant photosynthetic pigments, i.e., chlorophyll and carotenoids. Taken together, our study suggested that P. lilacinum can be used as a plant growth-promoting fungus and a biological nematicide for disease management of root-knot nematodes in eggplants.

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Section: Introductionmentioning

confidence: 99%

Purpureocillium lilacinum for plant growth promotion and biocontrol against root-knot nematodes infecting eggplant

Khan

Tanaka

2023

PLoS ONE

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“…In response to disease stress, the internal physiological structure and biochemical properties of plants are altered [9]. A certain extent of leaf yellowing, wilting and withering will be exhibited in appearance, along with a change in spectral features [10]. Diseases can be detected at the plant, canopy and larger scale of crop based on the differences in spectral features [11].…”

Section: Introductionmentioning

confidence: 99%

Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

Hou

Zhang

et al. 2022

Agriculture

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Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The determination of disease severity was accomplished by two methods directly or indirectly based on differences in reflectance. One approach was to utilize ML algorithms to build a model that directly reflects the relationship between disease level and spectral reflectance. Another method was to first use partial least squares to construct a predictive model of internal physicochemical values, such as relative chlorophyll content (SPAD) and peroxidase (POD) activity, and then use an ML model to classify disease levels based on the predicted values. The classification accuracy based on these two methods could reach up to 99 and 95%, respectively. The changes in physicochemical values during the development of disease were further investigated. Regression models for fitting changes in SPAD value and POD activity were developed based on temperature and incubation time, with determination coefficients of 0.961 and 0.997, respectively. The prediction of epidemic period was realized by combining regression and classification models based on physicochemical values with an accuracy of 88.5%. It is demonstrated that rapid non-destructive determination of physicochemical values based on Vis/NIR spectroscopy for potato late blight detection is feasible. Furthermore, it is possible to guide the control of disease throughout the epidemic period.

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“…Potato leaves infected with early blight will wilt prematurely [ 2 ], while potato leaves infected with late blight will produce many green spots. Disease damage increases with changes in the external environment; for example, during the rainy season, when humidity is high and favours rapid fungal growth, spots can proliferate within one to two days, causing potato rhizome to become brittle and cracked [ 3 ], seriously affecting potato yields. Therefore, it is essential to establish a rapid and effective method for detecting early and late blight of potatoes.…”

Section: Introductionmentioning

confidence: 99%

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

Dai

Fan

2022

Computational Intelligence and Neuroscience

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To solve the problems of weak generalization of potato early and late blight recognition models in real complex scenarios, susceptibility to interference from crop varieties, colour characteristics, leaf spot shapes, disease cycles and environmental factors, and strong dependence on storage and computational resources, an improved YOLO v5 model (DA-ActNN-YOLOV5) is proposed to study potato diseases of different cycles in multiple regional scenarios. Thirteen data augmentation techniques were used to expand the data to improve model generalization and prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. Based on this, the features extracted from all network layers are visualized, and the extraction of features from each network layer can be distinguished, from which an understanding of the feature learning behavior of the deep model can be obtained. The results show that in the scenario of multiple complex factors interacting, the identification accuracy of early and late potato blight in this study reached 99.81%. The introduced data augmentation technique improved the average accuracy by 9.22%. Compared with the uncompressed YOLO v5 model, the integrated ActNN runs more efficiently, the accuracy loss due to compressed parameters is less than 0.65%, and the time consumption does not exceed 30 min, which saves a lot of computational cost and time. In summary, this research method can accurately identify potato early and late blight in various scenarios.

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Common potato disease symptoms: ambiguity of symptom-based identification of causal pathogens and value of on-site molecular diagnostics

Cited by 11 publications

References 100 publications

Purpureocillium lilacinum for plant growth promotion and biocontrol against root-knot nematodes infecting eggplant

Purpureocillium lilacinum for plant growth promotion and biocontrol against root-knot nematodes infecting eggplant

Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

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