Spectral characteristics of winter wheat varieties depending on the development degree of Pyrenophora tritici-repentis

Kremneva, O. Yu.; Danilov, Roman; Sereda, I. I.; Tutubalina, Olga; Pachkin, A. A.; Зимин, М.В.

doi:10.1007/s11119-022-09976-2

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…This leads to unreliable samples being used to train the algorithm, consequently reducing its accuracy. Second, new agricultural practices involving new winter wheat varieties can result in spectral characteristics differing significantly from the training samples [86,87]. The optimal migration threshold may not be suitable for identifying these new varieties, posing a challenge for the AGWWS.…”

Section: Issues For Agwws Algorithm and Mapping Winter Wheatmentioning

confidence: 99%

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Zhang,

Sun,

Sun

2024

Remote Sensing

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Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

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Section: Issues For Agwws Algorithm and Mapping Winter Wheatmentioning

confidence: 99%

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Zhang,

Sun,

Sun

2024

Remote Sensing

View full text Add to dashboard Cite

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“…The difference in the spectral responses of different varieties of the same crop also presents significant difficulties. Therefore, the model for diagnosing the development of the disease in one particular wheat variety may not be applicable to another variety [ 14 , 30 , 31 ]. Finally, there is a need to study the dynamics of changes in the spectral images of cultivated crops against the background of the development of diseases over time during their growing season [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…To create an infectious background on selected test plots, the method of artificial infection of winter wheat plants with spores of phytopathogens was used. Infection of winter wheat plants was carried out on April 16 in the "beginning of the tube" phase (GS [30][31][32]. A 1:100 mixture of urediniospores with talc was used for plant inoculation at a loading of 5 mg spores/m 2 .…”

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confidence: 99%

Development of Methods for Remote Monitoring of Leaf Diseases in Wheat Agrocenoses

Sereda,

Danilov,

Kremneva

et al. 2023

Plants

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The development of remote methods for diagnosing the state of crops using spectral equipment for remote sensing of the Earth and original monitoring tools is the most promising solution to the problem of monitoring diseases of wheat agrocenoses. A research site was created on the experimental field of the Federal Research Center of Biological Plant Protection. Within the experimental field with a total area of 1 ha, test plots were allocated to create an artificial infectious background, and the corresponding control plots were treated with fungicides. The research methodology is based on the time synchronization of high-precision ground-based spectrometric measurements with satellite and unmanned remote surveys and the comparison of the obtained data with phytopathological field surveys. Our results show that the least-affected plants predominantly had lower reflectance values in the green, red, and red-edge spectral ranges and high values in the near-infrared range throughout the growing season. The most informative spectral ranges when using satellite images and multispectral cameras placed on UAVs are the red and IR ranges. At the same time, the high frequency of measurements is of key importance for determining the level of pathogenic background. We conclude that information acquisition density does not play as significant of a role as the repetition of measurements when carrying out ground-based spectrometry. The use of vegetation indices in assessing the dynamics of the spectral images of various survey systems allows us to bring them to similar values.

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Application of Fluorescence Spectroscopy for Early Detection of Fungal Infection of Winter Wheat Grains

Matveeva,

Sarimov,

Persidskaya

et al. 2024

AgriEngineering

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Plant pathogens are an important agricultural problem, and early and rapid pathogen identification is critical for crop preservation. This work focuses on using fluorescence spectroscopy to characterize and compare healthy and fungal pathogen-infected wheat grains. The excitation–emission matrices of whole wheat grains were measured using a fluorescence spectrometer. The samples included healthy control samples and grains manually infected with Fusarium graminearum and Alternaria alternata fungi. The five distinct zones were identified by analyzing the location of the fluorescence peaks at each measurement. The zone centered at λem = 328/λex= 278 nm showed an increase in intensity for grains infected with both pathogens during all periods of the experiment. Another zone with the center λem = 480/λex = 400 nm is most interesting from the point of view of early diagnosis of pathogen development. A statistically significant increase of fluorescence for samples with F. graminearum is observed on day 1 after infection; for A. alternata, on day 2, and the fluorescence of both decreases to the control level on day 7. Moreover, shifts in the emission peaks from 444 nm to 452 nm were recorded as early as 2–3 h after infection. These results highlight fluorescence spectroscopy as a promising technique for the early diagnosis of fungal diseases in cereal crops.

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Spectral characteristics of winter wheat varieties depending on the development degree of Pyrenophora tritici-repentis

Cited by 5 publications

References 47 publications

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Development of Methods for Remote Monitoring of Leaf Diseases in Wheat Agrocenoses

Application of Fluorescence Spectroscopy for Early Detection of Fungal Infection of Winter Wheat Grains

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