Monitoring insect pest infestation via different spectroscopic techniques

Ahmad, Mohd Najib; Shariff, Abdul Rashid Mohamed; Moslim, Ramle

doi:10.1080/05704928.2018.1445094

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…Therefore, monitoring chlorophyll content effectively reflects the growth condition of crops. Variations in grayscale values on hyperspectral scanning recordings are caused on a broad scale when the crop is damaged by pests or disease, resulting in considerable variances in spatial, spectral, and temporal phases (Liu et al, 2017;Ahmad et al, 2018). The introduction of fused hyperspectral data and chlorophyll feature content approaches by analyzing local spectral differences of crops may also enhance remote sensing research on the monitoring of pests and diseases (Vanegas et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

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Section: Introductionmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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show abstract

“…Due to hyperspectral imaging can obtain spatial and spectral information of the research object, both spatial and spectral information in hyperspectral imaging can be used to detect pest infection and disease infection ( Ahmad et al, 2018 ). Therefore, the spectral information and spatial information based on hyperspectral imaging have been introduced in previous research.…”

Section: Discussionmentioning

confidence: 99%

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

Yan

Jiao

et al. 2021

Front. Plant Sci.

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Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376–1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.

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“…Teknik spektroskopi dan pencitraan, seperti fluoresensi light detection and range (LIDAR) serta spektroskopi Vis/NIR digunakan untuk menentukan sifat spektral hama. Teknik-teknik ini terbukti menjadi metode yang dapat diandalkan untuk pemantauan yang lebih baik terhadap pergerakan serangga hama [23].…”

Section: Detektor Elektronikunclassified

Kajian Algoritma Deteksi Hama Gudang Menggunakan Metode Pengukuran Intensitas Pantulan Cahaya Objek

Hudaya

Budiawan²,

Sutian³

et al. 2021

IRWNS

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Monitoring insect pest infestation via different spectroscopic techniques

Cited by 14 publications

References 45 publications

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

Kajian Algoritma Deteksi Hama Gudang Menggunakan Metode Pengukuran Intensitas Pantulan Cahaya Objek

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