Use hyperspectral remote sensing technique to monitoring pine wood nomatode disease preliminary

Lin, Qingyu; Wang, Xianghong; Jiang, J. H.; Yang, Xianchang; Ke, Daiyan; Hong-qun, LI; Wang, Dingyi

doi:10.1117/12.2247214

Cited by 7 publications

(5 citation statements)

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“…Esse estudo foi realizado, utilizando-se da câmera VNIR A-series (Headwall, EUA), acoplada a uma aeronave tripulada. Após avaliação in loco, ortomosaico gerado a partir de imagens RGB e análise dos dados hiperespectrais, os resultados indicam que a distribuição das arvores sintomáticas foi a mesma nas três metodologias, porém mais fácil de se atingir, e com maior acurácia, com o sensoriamento hiperespectral (QIN et al, 2016). Em uma revisão mais recente sobre este mesmo assunto, são abordadas ainda outras maneiras de se adquirir e de se processar esses dados, por exemplo, com sensores acoplados a RPAs (WU et al, 2020).…”

Section: Sensores Hiperespectraisunclassified

Monitoramento Fitossanitário Utilizando Sensoriamento Remoto: Avanços e Desafios

Barros

Rosalen²,

Filho

et al. 2021

Rev. Bras. Cartogr.

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Problemas fitossanitários, como doenças, pragas e plantas daninhas, que competem com as plantas cultivadas por recursos e, consequentemente, prejudicam seu pleno desenvolvimento, são responsáveis por perdas significativas no setor agrícola, ano após ano. Portanto, conhecer o status sanitário da lavoura é um fator crucial no planejamento de ações de manejo, além de embasar políticas públicas de investimento e de proteção, a serem adotadas, com o objetivo de prevenir perdas produtivas e de assegurar a segurança alimentar da nação. Quando expostas a esses problemas, as plantas ativam respostas de defesa, cujos mecanismos moleculares são muito complexos. Nos estágios iniciais de um ataque de pragas ou de uma doença, embora os sintomas ainda não sejam visíveis no dossel, as plantas reagem com diferentes mecanismos fisiológicos, tais como a redução da taxa de fotossíntese, que induz a um aumento da fluorescência e da emissão de calor. Dessa forma, plantas estressadas produzem uma assinatura espectral que difere da assinatura de uma planta saudável. Dentro do Sensoriamento Remoto, inúmeras pesquisas têm estudado a relação entre diferentes problemas fitossanitários com a resposta espectral registrada em sensores multi ou hiperespectrais (sejam imageadores ou não imageadores, ativos ou passivos e embarcados em plataformas terrestres, aéreas ou orbitais). Nesse contexto, o objetivo desta revisão é descrever o estado da arte da tecnologia de Sensoriamento Remoto aplicada a aspectos fitossanitários de lavouras, nos níveis de coleta de dados terrestre, aéreo e orbital, envolvendo diferentes modalidades de sensores.

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Section: Sensores Hiperespectraisunclassified

Monitoramento Fitossanitário Utilizando Sensoriamento Remoto: Avanços e Desafios

Barros

Rosalen²,

Filho

et al. 2021

Rev. Bras. Cartogr.

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“…Compared with ground remote sensing and high-altitude remote sensing, low-altitude remote sensing with unmanned aerial vehicles (UAVs) is flexible and efficient and has the advantage of yielding low-cost, high-spatial-resolution, and high PWD detection accuracy [20,21]. The HI generated by a UAV hyperspectral imaging spectrometer has great potential in detecting PWD with its abundant spatial and spectral information [22][23][24]. Huang et al [25] used a fixed-wing UAV equipped with a multispectral digital camera to collect the normalized differential vegetation index (NDVI) of pine trees and recognized the dead pine trees caused by PWD with 80% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery

Zhang

Hong²,

Huang³

et al. 2022

Applied Sciences

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Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, it is necessary to establish a feasible and effective method to accurately monitor and estimate PWD infection. In this study, we used hyperspectral imagery (HI) collected by an unmanned airship with a hyperspectral imaging spectrometer to detect PWD in healthy, early, middle and serious infection stages. To avoid massive calculations on the full spectral dimensions of the HI, 16 spectral features were extracted from the HI, and a genetic algorithm (GA) was implemented to identify the optimal ones with the least fitness. Simultaneously, a support vector machine (SVM) classifier was established to predict the PWD infection stage for an individual pine tree. The following results were obtained: (1) the spectral characteristics for pine trees in different PWD infection stages were distinctive in the green region (510–580 nm), red edge (680–760 nm) and near-infrared (780–1000 nm) spectra; (2) the six optimal spectral features (Dgreen, SDgreen, Dred, DRE, DNIR, SDNIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests.

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“…Remote sensing technique has the characteristics of long distance, large area and indirect contact, and has been widely used in forest monitoring. Currently, this technique is used to carry out PWD detection, mainly using satellite images (Lee, 2006), (Takenaka, 2017) and aerial hyperspectral images (Pan, 2015), (Qin, 2016). Unfortunately, under the cloudy and rainy conditions in southern China, it is difficult to obtain highresolution and large-area images of these two types in time, and the timeliness of the observation data cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Extraction of the Individual Tree Infected by Pine Wilt Disease Using Unmanned Aerial Vehicle Optical Imagery

Zhou

Liao

Cheng

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. For eliminating pine trees infected pine wilt disease in southern China based on remote sensing technique, it is important to ensure the provision of timely information about individual diseased tree. It is not easy to detect and extract the diseased pine trees from conventional remote sensing techniques. This paper proposes a new approach for extracting information about individual diseased tree, without the use of satellite images and aerial hyperspectral images. Field measurements in different leaf infected stages indicates the possibility of extracting diseased trees by using only the three regular bands, red, green and blue. VEG was selected and proved to be the optimal index in 12 vegetation indices from the three visible bands. Using the adaptive local threshold selection methods, VEG grayscale image pixels could be automatically segmented into the diseased trees region. Based on mathematical morphology, the accuracy of individual tree information extraction reached 90%.

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Use hyperspectral remote sensing technique to monitoring pine wood nomatode disease preliminary

Cited by 7 publications

References 0 publications

Monitoramento Fitossanitário Utilizando Sensoriamento Remoto: Avanços e Desafios

Monitoramento Fitossanitário Utilizando Sensoriamento Remoto: Avanços e Desafios

A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery

Extraction of the Individual Tree Infected by Pine Wilt Disease Using Unmanned Aerial Vehicle Optical Imagery

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