Application of aerial remote sensing technology for detection of fire blight infected pear trees

Bagheri, Nikrooz

doi:10.1016/j.compag.2019.105147

Cited by 54 publications

(21 citation statements)

References 43 publications

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“…In this study, we selected several vegetation indices, moisture indices and soil brightness index variables to which SOC is sensitive. Among the vegetation indices selected were the normalized difference vegetation index (NDVI) [ 46 , 47 ], enhanced vegetation index (EVI) [ 48 ], difference vegetation index (DVI) [ 49 ], ratio vegetation index (RVI) [ 50 ], transformation vegetation index (TVI) [ 51 ], soil-adjusted vegetation index (SAVI) [ 52 ], soil-adjusted total vegetation index (SATVI) [ 53 ] and normalized burn ratio 2 (NBR2) [ 19 ]. The moisture indices include land surface water index (LSWI) [ 54 ] and moisture stress index (MSI) [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Xie

Ding

et al. 2022

Sensors

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Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

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Section: Methodsmentioning

confidence: 99%

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Xie

Ding

et al. 2022

Sensors

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“…These applications have been extended to other species and crops, namely, to discriminate between multiple cultivars of a crop species [59], to predict critical crops yield, and in the early detection of potentially dangerous diseases. These include the laurel wilt disease in avocado [60]; fire blight in pear trees [61,62]; canker, black spot, decay, and Huanglongbing (HLB) in citrus [1,10,11,32,63]; Yellow Rust in wheat [64]; Southern Corn Rust (SCR) [65]; the Potato Virus Y (PVY) disease in visually asymptomatic infected potato plants [59]; or to identify and classify grapevines inoculated with the Grapevine Vein-Clearing Virus (GVCV) at the early asymptomatic stages, under field conditions [57].…”

Section: Elastic Spectroscopymentioning

confidence: 99%

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

et al. 2022

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As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

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“…Non-destructive methods have been utilized as remote sensing tools for identifying and evaluating diseases occurring over a season ( Immerzeel et al, 2008 ). Bagheri (2020) applied aerial multispectral imagery for the detection of fire blight infected pear trees by utilizing unmanned aerial vehicle (UAV), and several vegetation indices (VIs) (IPI, RDVI, MCARI1, MCARI2, TVI, MTVI1, MTVI2, TCARI, PSRI, and ARI) were evaluated for disease detection. The support vector machine (SVM) method was used to detect diseased trees with an accuracy of 95%.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning

et al. 2022

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Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380–1,000 nm) and in the field by a UAV-based imaging system (380–1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86–90% for the laboratory analysis and 69–91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700–900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications.

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Application of aerial remote sensing technology for detection of fire blight infected pear trees

Cited by 54 publications

References 43 publications

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning

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