Development of an early warning system for brown planthopper (BPH) (Nilaparvata lugens) in rice farming using multispectral remote sensing

Ghobadifar, Faranak; Wayayok, Aimrun; Jebur, Mustafa Neamah

doi:10.1007/s11119-015-9422-9

Cited by 11 publications

(5 citation statements)

References 23 publications

(24 reference statements)

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“…In multispectral images, the representative bands have different ranges. For example, the RED value in the SPOT-5 satellite band ranges from 610-680 nm [26][27][28] while in PlanetScope in the range of 590-670 nm [23]. The Terra MODIS satellite even has a dedicated NDVI channel in its imagery [24,25].…”

Section: Sensitive Wavelength Determination Algorithmmentioning

confidence: 99%

“…The models produced by the studies had been able to model the damage level [12,13,19,20], BPH population [11,24,25,27,29,31], distinguish between healthy and infested plants [17,22,23,28,39], and estimate crop losses [21,30]. Generally, research using satellite imagery can only model the level of infestation in a little detail as the use of terrestrial sensors.…”

Section: Model Algorithmmentioning

confidence: 99%

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Rice brown planthopper monitoring and detection by spectral reflectance: a review

Arifin,

Koesmaryono,

Impron

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Brown planthopper (BPH) has been one of the main pests of rice worldwide. Monitoring is important factor for determining attacks and estimating their effects. The traditional monitoring approach is usually conducted through visual observation and field scouting, with limitations such as subjectivity and time consumption. Remote sensing is an alternative pest monitoring method that covers a larger area in a shorter time. This paper discusses a remote-sensing method that uses a spectral approach to detect BPH attacks. Literature was filtered and processed using the PRISMA method. According to the spectral sensor, studies were classified into multispectral and hyperspectral sensors. Based on this scale, there are four studies on the panicle, leaf, canopy, and field levels. The model used single-wave reflectance and spectral indices as predictors. Various algorithms were used in the studies: linear regression, Principal Component Analysis, and Machine Learning to estimate the severity class, BPH Population density, and yield loss. A combination of spectral reflectance with other parameters, such as weather, fertilizer application, and infestation time, was conducted to improve the performance of the detection model. This review provides state-of-the-art spectral reflectance usage for detecting BPH attacks and opportunities for future development.

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Section: Sensitive Wavelength Determination Algorithmmentioning

confidence: 99%

Section: Model Algorithmmentioning

confidence: 99%

Rice brown planthopper monitoring and detection by spectral reflectance: a review

Arifin,

Koesmaryono,

Impron

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Söderström et al [47] predicted protein content in malting barley using proximal sensing and SPOT 5 images. Ghobadifar et al [48] developed an early warning system for brown planthopper (Nilaparvata lugens) in rice farming using SPOT 5 images. The 10-m SPOT 5 sensor was decommissioned in 2015, but the 6-m SPOT 6 and 7 sensors continue to provide images.…”

Section: Applications Of High Resolution Satellite Sensors In Precisimentioning

confidence: 99%

High resolution satellite imaging sensors for precision agriculture

Yang

2018

Front. Agr. Sci. Eng.

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The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications, including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture are discussed.

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“…These methods have the obvious characteristics of low efficiency, high labor intensity, and high professional requirements. Although the image recognition and counting techniques ( Yao et al., 2014 ; Yu et al., 2019 ) and spectral remote sensing ( Huang et al., 2015 ; Ghobadifar et al., 2016a ; Ghobadifar et al., 2016b ; Liu and Sun, 2016 ) have been found to have potential for estimating the density of rice planthoppers, their practical application is still rare because these methods are restricted by environmental conditions. Therefore, a practical, simple, and automatic technique is demanded and promising in rice planthopper monitoring.…”

Section: Introductionmentioning

confidence: 99%

Estimating insect pest density using the physiological index of crop leaf

Chen,

Liu

2023

Front. Plant Sci.

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Estimating population density is a fundamental study in ecology and crop pest management. The density estimation of small-scale animals, such as insects, is a challenging task due to the large quantity and low visibility. An herbivorous insect is the big enemy of crops, which often causes serious losses. Feeding of insects results in changes in physiology-related chemical compositions of crops, but it is unknown whether these changes can be used to estimate the population density of pests. The brown planthopper (BPH), Nilaparvata lugens, is a serious insect pest hiding under rice canopy to suck the sap of rice stems. BPH density is a crucial indicator for determining whether the control using pesticides will be carried out or not. Estimating BPH density is still dependent on manmade survey and light-trap methods, which are time-consuming and low-efficient. Here, we developed a new method based on the physiological traits of rice leaves. The feeding of BPHs significantly decreased the contents of chlorophyll (the SPAD readings), water, silicon, and soluble sugar in rice leaves. Four ratio physiological indices based on these four physiological traits of the BPH-damaged rice leaves to those of healthy leaves were established, and they were significantly correlated with BPH density in rice plants. A rice growth stage-independent linear model based on the four ratio physiological indices and adding the other two variables, BPH damage duration and population increase rate, was developed. This model exhibited a reasonable accuracy for estimating BPH density. This new method will promote the development of density estimation of pest populations toward nonprofessionalization and automation.

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Development of an early warning system for brown planthopper (BPH) (Nilaparvata lugens) in rice farming using multispectral remote sensing

Cited by 11 publications

References 23 publications

Rice brown planthopper monitoring and detection by spectral reflectance: a review

Rice brown planthopper monitoring and detection by spectral reflectance: a review

High resolution satellite imaging sensors for precision agriculture

Estimating insect pest density using the physiological index of crop leaf

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