Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery

Yuan, Lin; Bao, Zhiyan; Zhang, Haibo; Zhang, Yuntao; Liu, Xi

doi:10.1016/j.ijleo.2017.06.071

Cited by 58 publications

(46 citation statements)

References 12 publications

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“…It attempts to find a linear transformation that maximizes the dispersion between classes and minimizes the dispersion within the class to separate one class from the others [29]. FLDA is commonly used in recognition, classification and feature extraction [10,[30][31][32][33]. The existing successful cases support the use of FLDA in this study for the identification of Fusarium head blight in wheat ears.…”

Section: Introductionmentioning

confidence: 73%

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Huang

Jing

et al. 2019

Sensors

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Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

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Section: Introductionmentioning

confidence: 73%

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Huang

Jing

et al. 2019

Sensors

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“…The auxiliary area (114 • 57 3 E, 37 • 55 51 N) was a suburban area of Shijiazhuang, in Hebei province. This region also has a temperate humid continental monsoon climate with an annual mean temperature of 12-13 • C and annual precipitation of 400-800 mm [6,33]. Abundant sunshine and suitable temperature make it suitable for crop growth.…”

Section: Study Area and Datamentioning

confidence: 99%

“…The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were inversed to measure crop growth [41][42][43]. Wetness and greenness, which are generated using tasseled cap transformation, were also inversed to indicate the field habitat characteristics [6]. These indices had been used to monitor wheat powdery mildew in previous studies [6,44] and were calculated using the following equations:…”

Section: Remote Sensing Datamentioning

confidence: 99%

Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

et al. 2019

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In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in sampling restricts the accuracy of monitoring regional prevalence of the disease. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. By taking into account the representativeness of contributions of auxiliary samples to adjust the weight placed on auxiliary samples, an optimized TrAdaBoost algorithm, named OpTrAdaBoost, was generated to map regional wheat powdery mildew. The algorithm conducts this by: (1) producing uncertainty associated with each prediction based on the similarities, and calculating the representativeness contribution of all auxiliary samples by taking into account the overall uncertainty of the wheat powdery mildew map; (2) calculating the errors of the weak learners during the training process and using boosting to filter out the unreliable auxiliary samples by adjusting the weights of auxiliary samples; (3) combining all weak learners according to the weights of training instances to build a strong learner to classify disease severity. OpTrAdaBoost was tested using a dataset with 39 study area samples and 106 auxiliary samples. The overall monitoring accuracy was 82%, and the kappa coefficient was 0.72. Moreover, OpTrAdaBoost performed better than other algorithms that are commonly used to monitor wheat powdery mildew at the regional level. Experimental results demonstrated that OpTrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

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“…Remote sensing can be employed to potentially estimate the possibility of occurrence of plant diseases and pests in a particular location. Plant diseases and the pest occurrence is evaluated by monitoring leaf, canopy, and field levels [56]. Another approach in remote sensing focuses on Radiation Use Efficiency (RUE), Light Use Efficiency (LUE) to state that total biomass production is directly proportional to total photosynthetic active radiation absorption [25].…”

Section: Remote Sensingmentioning

confidence: 99%

Role of Big Data in Agriculture

J*¹,

Indiramma²

2019

IJITEE

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Big Data analytics in the agricultural sector has huge potential to cater to requirements of food production. This review highlights the role of Big Data in pertinent data acquisition from factors affecting the agriculture such as weather, soil, diseases, remote sensing and the prospects of agricultural data analysis towards smart farming. Incorporating modern technologies in farming practices continuously monitor the environment, thus producing large quantity of data. Hence there arises the need for advanced practical and systematic strategies to correlate the different factors driving the agriculture to derive valuable information out of it. Big Data can be a promising aspect for the future of food production and sustainability of agriculture. Leveraging big data in the agricultural sector can provide insights in farming practices, helps in making real-time decisions and motivates in incorporating new methods of farming operations. The main objective of this paper is to provide insights into different factors that contribute to making timely recommendations to farmers with regard to smart agricultural techniques.

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Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery

Cited by 58 publications

References 12 publications

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

Role of Big Data in Agriculture

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