Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

An, Gangqiang; Xing, Minfeng; He, Bin; Kang, Haiqi; Shang, Jiali; Liao, Chunhua; Huang, Xiaodong; Zhang, Hongguo

doi:10.3390/rs13163185

Cited by 9 publications

(6 citation statements)

References 43 publications

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“…According to the band selection results of genetic algorithm (see Figure 5b and Table 6), the selected characteristic bands are mainly 698-800 nm and 974-997 nm, which are consistent with the sensitive bands of RFS studied [11,27].…”

Section: Screening Results Of Spectral Characteristic Bands By Geneti...supporting

confidence: 62%

“…Although RFS is mainly concentrated on small areas around the original disease source area, the common practice is still to spray pesticides indiscriminately on the entire field [10]. In order to minimize the economic losses and environmental pollution caused by pesticides, it is necessary to accurately assess the distribution and prevalence of RFS [11]. Therefore, an automated, nondestructive, fast, sensitive and selective method is urgently needed to quickly detect plant diseases and reduce the use of pesticides and fertilizers to support sustainable agricultural production [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…In the existing research, some use the machine learning classification method Support Vector Machine (SVM) and histogram analysis method [1,8,23] to select features, some use the neural network (CNN) to build models [25] for the whole band or use genetic algorithm [23,24] to extract feature bands. Many more researchers use the Random Forest (RF) model [3,11,20,22] to establish crop disease and pest detection models and achieve high accuracy. In summary, spectral analysis of hyperspectral images and calculation of spectral parameters are the primary methods for pest and disease monitoring, and the use of machine learning models and deep learning networks is a hot research direction.…”

Section: Introductionmentioning

confidence: 99%

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Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

et al. 2023

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Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles.

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Section: Screening Results Of Spectral Characteristic Bands By Geneti...supporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

et al. 2023

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“…So far, this technology has not been widely used in disease detection of medicinal plants. Most disease detection and identification of crops or other plants are performed on images of plant tissues where the diseases occur with visible symptoms ( Singh et al., 2018 ; Singh et al., 2021 ), such as leaf blast and false smut infection of rice ( An et al., 2021 ; Tian et al., 2021 ) and apple fire blight disease ( Jarolmasjed et al., 2019 ). However, the medicinal parts of most medicinal plants, like ginsengs, are roots, whose hyperspectral reflectance data cannot be collected directly and non-constructively.…”

Section: Discussionmentioning

confidence: 99%

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance

et al. 2022

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Ginseng is an important medicinal plant benefiting human health for thousands of years. Root disease is the main cause of ginseng yield loss. It is difficult to detect ginseng root disease by manual observation on the changes of leaves, as it takes a long time until symptoms appear on leaves after the infection on roots. In order to detect root diseases at early stages and limit their further spread, an efficient and non-destructive testing (NDT) method is urgently needed. Hyperspectral remote sensing technology was performed in this study to discern whether ginseng roots were diseased. Hyperspectral reflectance of leaves at 325-1,075 nm were collected from the ginsengs with no symptoms on leaves at visual. These spectra were divided into healthy and diseased groups according to the symptoms on roots after harvest. The hyperspectral data were used to construct machine learning classification models including random forest, extreme random tree (ET), adaptive boosting and gradient boosting decision tree respectively to identify diseased ginsengs, while calculating the vegetation indices and analyzing the region of specific spectral bands. The precision rates of the ET model preprocessed by savitzky golay method for the identification of healthy and diseased ginsengs reached 99% and 98%, respectively. Combined with the preliminary analysis of band importance, vegetation indices and physiological characteristics, 690-726 nm was screened out as a specific band for early detection of ginseng root diseases. Therefore, underground root diseases can be effectively detected at an early stage by leaf hyperspectral reflectance. The NDT method for early detection of ginsengs root diseases is proposed in this study. The method is helpful in the prevention and control of root diseases of ginsengs to prevent the reduction of ginseng yield.

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“…The automatic detection method of crop disease severity level has become increasingly important in the field of agricultural diseases and pests [2,7,10]. The crops have been treated with inoculum in most of the studies on crop diseases and pests [23,33], which has made it easier to detect the infected samples. In this study, the cotton field was in a natural environment, without any inoculum treatment, and the cotton aphids were the result of natural processes.…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Grade Cotton Aphid Damage Severity with Hyperspectral Index Reconstruction

Qiao

Chen

et al. 2022

Applied Sciences

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As a kind of important insect pest of cotton crops, aphids cause serious damage in cotton yields and quality worldwide, posing a significant risk to economic losses. Automatic detection of the pest damage level plays an important role in cotton field management. However, it is usually regarded as a classification problem in machine learning, where the disease severity levels are taken as independent categories and the inter-level relationship has not fully been considered. To utilize the inherited relations among different severity levels caused by cotton aphids, a novel approach based on the spectral index reconstruction was proposed in this study. First, six types of initial spectral indices were reconstructed based on healthy samples in the training set. Then, the severity sequences corresponding to the reconstructed initial spectral indices (RISIs) were sorted and compared with the ideal sequence. After attaining sequences most consistent with the ideal one, the ratio between the inter- and intra- levels was calculated to select the sensitive RISI. Moreover, the range of each severity level was established by the thresholds between adjacent grades of the selected sensitive RISI, which was finally used to determine the disease severity level caused by cotton aphids. Results of the cotton aphids showed that the proposed approach achieved a grading performance with OA = 0.944, AA = 0.900, and Kappa coefficient = 0.928. Hence, the proposed approach based on hyperspectral index reconstruction is effective and has potential application in grading the aphid infestation severity of cotton.

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Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Cited by 9 publications

References 43 publications

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

Rice False Smut Monitoring Based on Band Selection of UAV Hyperspectral Data

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance

A Novel Approach to Grade Cotton Aphid Damage Severity with Hyperspectral Index Reconstruction

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