A remote sensing assessment of pest infestation on sorghum

Singh, Dharmendra; Sao, R.; Singh, Kumar Hemant

doi:10.1016/j.asr.2006.02.025

Cited by 25 publications

(12 citation statements)

References 13 publications

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“…Hyperspectral remote sensing is a new technique that utilizes sensors operating in hundreds of contiguous narrow wavelength bands which may have potential to improve the assessment of crop disease (Carter, 1994;Adams et al, 1999;Singh et al, 2007). Numerous studies demonstrate that hyperspectral reflectance and its detection accuracy of plant biochemicals were affected by the autocorrelation and multicollinearity of the data due to the continuous wavebands (Card et al, 1988;Yoder and Pettigrew-Crosby, 1995).…”

Section: Introductionmentioning

confidence: 99%

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

Liu

Huang

Shi

et al. 2007

J. Zhejiang Univ. - Sci. B

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Abstract:Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

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Section: Introductionmentioning

confidence: 99%

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

Liu

Huang

Shi

et al. 2007

J. Zhejiang Univ. - Sci. B

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“…RS multispectral and hyperspectral sensors are swiftly generating vast amounts of data in a cost‐effective manner and at higher spatial and spectral resolutions. Hyperspectral and multispectral images, consisting of reflectance from the visible, near infrared, and midinfrared regions of the electromagnetic spectrum, can be interpreted in terms of physical parameters for instance crop cover, crop health, and soil moisture) and are useful for operations such as stress mapping, fertilization and pesticide application, and irrigation management (Barnes & Baker, ; Barroso, Payan, & Vivoni, ; Hinzman, Bauer, & Daughtry, ; Lelong, Pinet, & Poilve, ; Pal & Mather, ; Singh, Sao, & Singh, ; Tilling, O'Leary, Ferwerda, Jones, & Fitzgerald, ; Yang, Prasher, Enright, Madramootoo, & Burgess, ). Nutrient contents of different crops such as wheat (Lelong et al, ; Silva & Beyl, ; Tilling et al, ), paddy rice (Stroppiana, Boschetti, Brivio, & Bocchi, ), sorghum, corn (Samson et al, ), broccoli (Shikha, Waller, Hunsaker, Clarke, & Barnes, ), citrus (Min, Lee, Burks, Jordan, & Schumann, ), grape (Smart, Whiting, & Stockert, ), and apple (Perry & Davenport, ) have also been assessed using hyperspectral and multispectral RS data.…”

Section: Literature Reviewmentioning

confidence: 99%

A review: The role of geospatial technology in precision agriculture

Praveen

Sharma

2019

Journal of Public Affairs

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Precision agriculture is a forthcoming farming management approach that is modifying the style of people farm. As of now, the accountability is increasing to reduce dependency of harming chemicals in agriculture. Various methods and techniques have been using in agriculture production to make it safer and diminish their harm impacts on environment to make agriculture sustainable. Precision agriculture is important tool to bring successful conclusion. This literature review focuses on geospatial technology can be an effective method in precision agriculture.

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“…Furthermore, the leaf area index (LAI) is the most common measure for monitoring and detecting crop diseases in a range of crops; for example, rice (Xiao et al, 2002;Qin & Zhang, 2005;Ghobadifar et al, 2016), tomato (Zhang et al, 2002), wheat (Huang & Apan, 2006), and sugar beet (Mahlein et al, 2013). measure for monitoring and improved assessment of crop diseases (Adams et al, 1999;Singh et al, 2007;Das et al, 2015). For example, in order to promote the sustainable farming of rice crops, Gnyp et al (2014) estimated above-ground biomass using hyperspectral canopy sensing to obtain an optimal measurement.…”

Section: Introductionmentioning

confidence: 99%

Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

Yudarwati¹,

Hongo²,

Sigit³

et al. 2020

JAS

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This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively

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A remote sensing assessment of pest infestation on sorghum

Cited by 25 publications

References 13 publications

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

A review: The role of geospatial technology in precision agriculture

Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

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