Bacterial Diseases of Crops

Lamichhane, Jay Ram

doi:10.1016/bs.agron.2015.06.006

Cited by 13 publications

(1 citation statement)

References 76 publications

(74 reference statements)

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“…They were prepared from 48-h-old cultures of Pst grown in KB medium (peptone, 20.0g; K 2 HPO 4 , 1.5g; MgSO 4 , 1.5g; glycerol, 10 mL; agar, 15g; distilled water up to 1.0 liter), and of Xeu cultures grown in YDC medium (yeast extract, 10.0g; dextrose, 20.0g; CaCO 3 , 20.0g; agar, 15.0g; distilled water up to 1.0 liter). The inoculated plants were then covered with transparent polythene bags for 48 h to increase the relative humidity that fosters bacterial entry into plant tissues through natural openings such as stomata ( Lamichhane, 2015 ). Plants were monitored daily for symptom development for 18 days ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling

Reis Pereira,

Santos,

Tavares

et al. 2023

Front. Plant Sci.

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Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine – SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants’ defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

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Section: Methodsmentioning

confidence: 99%