Journal of Plant Protection Research

Marín-Ortiz, Juan Carlos; Carvajal, Lilliana María Hoyos; Botero-Fernández, Verónica

doi:10.24425/jppr.2019.129290

Cited by 8 publications

References 45 publications

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Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning

Castro-Valdecantos,

Egea,

Borrero

et al. 2024

Precision Agric

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Strawberry (Fragraria x ananassa) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (gs) and photosynthesis (A) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (gs and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.

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Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning

Castro-Valdecantos,

Egea,

Borrero

et al. 2024

Precision Agric

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Chemometric analysis using infrared spectroscopy and PCA-LDA for early diagnosis of Fusarium oxysporum in tomato

García-Barrera,

Meza-Zamora,

Noa-Carrazana

et al. 2024

J Plant Dis Prot

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The interaction of phytopathogenic organisms and plants generates physiological and biochemical changes in the latter. However, the effects on the plants are rarely visible in the infection first stages. Novel optical techniques can help to improve the early detection of phytopathogenic organisms in tomato without the plant sacrifice. In this work, infrared spectroscopy and chemometric methods were used to determinate the effects of Fusarium oxysporum in tomato plants cultivated in pots, analyzing fully expanded leaves. Fusarium oxysporum was molecular identified and its pathogenicity was tested in vitro. Three plants treatments were evaluated for 55 days post infection in pots in greenhouse under semi-controlled conditions: control, water stress, and fungal inoculated (1 × 108 conidia/mL). Phenotypical results were followed twice a week for eight weeks; the phenotypical characteristics were very similar in almost all sampling times except in height, especially in the first 27 days post infection, after this time the height was similar in the three treatments. The stalk and root-dried matter analysis do not show statistical differences; however, the infrared results, acquired from the adaxial surface of leaves, show differences in peaks associated with salicylic acid, jasmonic acid, abscisic acid, and proline in the first 27 days post infection. The principal component analysis–linear discriminant analysis were used to distinguish subtle biochemical changes between the three treatments, facilitating the early detection of the pathogen and its monitoring over time.

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Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

Lassalle

2021

Science of The Total Environment

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Journal of Plant Protection Research

Cited by 8 publications

References 45 publications

Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning

Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning

Chemometric analysis using infrared spectroscopy and PCA-LDA for early diagnosis of Fusarium oxysporum in tomato

Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

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