An Overview of the Special Issue on Plant Phenotyping for Disease Detection

Pineda, Mónica

doi:10.3390/rs13204182

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The second objective of this work was to develop an effective method for the detection of Xcc-infected oilseed rape plants based on computer vision and deep learning algorithms in each climatic condition. This method has been used with great success in the last few years due to the reduction in the cost of image sensors and the development of deep-learning classification methods [ 90 , 91 , 92 ]. In this work, thermal, MCFI, and hyperspectral reflectance measurements of mock-control and Xcc-infected oilseed rape leaves were taken using imaging sensors.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Pineda

Barón

2023

Plants

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Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.

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Section: Discussionmentioning

confidence: 99%

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Pineda

Barón

2023

Plants

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“…However, most studies have not considered the correlation between the two-dimensional spatial information in hyperspectral images and crop health status. Infected plants exhibit changes in external features, and textural information can quantify attributes of crop phenotypic structure and tissue arrangement, thus extracting more potential information from images [22,23]. In recent years, some studies have applied textural features in researching plant biochemical states, affirming the capability of combining spectral and image information to estimate plant physiological parameters [24,25].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Song,

Liu,

et al. 2024

Remote Sensing

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Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.

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An Overview of the Special Issue on Plant Phenotyping for Disease Detection

Abstract: According to the latest United Nations estimates in September 2021, the world’s population is now 7 [...]

Cited by 2 publications

References 8 publications

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

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