Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves

Rivera-Romero, C. A.; Palacios-Hernández, Elvia R.; Trejo‐Durán, M.; Rodríguez-Liñán, M.C.; Olivera-Reyna, Roberto; Morales-Saldaña, J.A.

doi:10.1117/1.jrs.14.044515

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…In Rivera-Romero et al [16], a statistical analysis was conducted for determining a timeline (Figure 2) with sampling days and phenological growth stages with the visual assessment of PM signs and symptoms. At T 1 , leaf development (S 1 ), lateral bud formation (S 2 ), and inflorescence emergence (S 5 ) during the first nine sampling days (D 1 -D 9 ) are considered.…”

Section: Proposed Powdery Mildew Damage Levelsmentioning

confidence: 99%

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Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors

Rivera-Romero,

Palacios-Hernández,

Vite-Chávez

et al. 2024

Inventions

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Constant monitoring is necessary for powdery mildew prevention in field crops because, as a fungal disease, it modifies the green pigments of the leaves and is responsible for production losses. Therefore, there is a need for solutions that assure early disease detection to realize proactive control and management of the disease. The methodology currently used for the identification of powdery mildew disease uses RGB leaf images to detect damage levels. In the early stage of the disease, no symptoms are visible, but this is a point at which the disease can be controlled before the symptoms appear. This study proposes the implementation of a support vector machine to identify powdery mildew on cucurbit plants using RGB images and color transformations. First, we use an image dataset that provides photos covering five growing seasons in different locations and under natural light conditions. Twenty-two texture descriptors using the gray-level co-occurrence matrix result are calculated as the main features. The proposed damage levels are ’healthy leaves’, ’leaves in the fungal germination phase’, ’leaves with first symptoms’, and ’diseased leaves’. The implementation reveals that the accuracy in the L * a * b color space is higher than that when using the combined components, with an accuracy value of 94% and kappa Cohen of 0.7638.

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Section: Proposed Powdery Mildew Damage Levelsmentioning

confidence: 99%

“…The feature selection process consists of finding the best set of features that allows us to differentiate the four levels of damage. Statistical methods are used for the selection process, the flow diagram of which is shown in detail in Figure 9 [16].…”

Section: Feature Selectionmentioning

confidence: 99%

Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors

Rivera-Romero,

Palacios-Hernández,

Vite-Chávez

et al. 2024

Inventions

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“…Usando la base de datos de firmas espectrales obtenidas por Rivera-Romero et al (2020), este estudio analizó las mediciones de reflectancia en hojas de cucurbitáceas infectadas con cenicilla polvorienta visualizadas en cuatro niveles de daño. Se consideraron un total de 840 firmas espectrales entre hojas sanas (T1), hojas con germinación fúngica (T2), hojas con primeros síntomas (T3) y hojas enfermas (T4).…”

Section: Metodologíaunclassified

Aprendizaje basado en coeficientes de fourier para la identificación de daño en plantas de cultivos

Rivera-Romero

Olivera-Reyna²,

Muñoz‐Minjares³

et al. 2022

Ciencia Latina

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El análisis de firmas espectrales es uno de los métodos de diagnóstico más utilizados para identificar enfermedades en las plantas. Con este fin, se deben considerar diferentes técnicas de adquisición de información para detectar los diferentes niveles de una enfermedades o plaga en particular, como en el caso de las enfermedades fúngicas. En este estudio, se consideraron plantas cucúrbitas en las cuales se identificaron tres etapas de niveles de una enfermedad fúngica que son las hojas en la etapa de germinación del hongo, hojas con primeros síntomas y hojas enfermas. Se utilizó una base de datos con firmas espectrales de hojas de calabacita. A continuación, se propone el análisis de frecuencia de las características espectrales utilizando la transformada de Fourier para extraer características de los coeficientes obtenidos y partir de bloques de clasificación con máquinas de vectores de soporte para la estimación del nivel de daño. Se demostraron precisiones de clasificación del 98.3%. Por lo tanto, este método se puede utilizar para diagnosticar el grado de daño en diferentes cultivos.

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“…First, they used an image dataset from five growing seasons in different locations in natural conditions of light. Twenty-two texture descriptors using the gray-level co-occurrence matrix result were calculated as the main features, and a statistical process [14] was used for the feature selection. The proposed damage levels identified were healthy leaves, leaves in germination time of the fungal, leaves with first symptoms, and diseased leaves.…”

Section: Introductionmentioning

confidence: 99%

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

Rivera-Romero,

Munoz-Minjares,

Lastre-Dominguez

et al. 2024

BDCC

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Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient’s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

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Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves

Cited by 4 publications

References 34 publications

Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors

Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors

Aprendizaje basado en coeficientes de fourier para la identificación de daño en plantas de cultivos

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

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