Dataset Information

Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.

ABSTRACT: An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R730, R815), and NDVI (R730, R815), respectively. R2 values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R2 values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.

SUBMITTER: Li H

PROVIDER: S-EPMC7285128 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.

Li Huaimin H Lin Weipan W Pang Fangrong F Jiang Xiaoping X Cao Weixing W Zhu Yan Y Ni Jun J

Sensors (Basel, Switzerland) 20200520 10

An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnos ...[more]

PMID: 32443796

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Project description:Background:Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scattering-based PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods:This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results:Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 ?g/m3, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions:Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

Project description:Evaluation of crop N status will assist optimal N management of intensive vegetable production. Simple procedures for monitoring crop N status such as petiole sap [NO 3(-)-N], leaf N content and soil solution [NO 3(-)] were evaluated with indeterminate tomato and muskmelon. Their sensitivity to assess crop N status throughout each crop was evaluated using linear regression analysis against nitrogen nutrition index (NNI) and crop N content. NNI is the ratio between the actual and the critical crop N contents (critical N content is the minimum N content necessary to achieve maximum growth), and is an established indicator of crop N status. Nutrient solutions with four different N concentrations (treatments N1-N4) were applied throughout each crop. Average applied N concentrations were 1, 5, 13 and 22 mmol L(-1) in tomato, and 2, 7, 13 and 21 mmol L(-1) in muskmelon. Respective rates of N were 23, 147, 421 and 672 kg N ha(-1) in tomato, and 28, 124, 245 and 380 kg N ha(-1) in muskmelon. For each N treatment in each crop, petiole sap [NO 3(-)-N] was relatively constant throughout the crop. During both crops, there were very significant (P < 0.001) linear relationships between both petiole sap [NO 3(-)-N] and leaf N content with NNI and with crop N content. In indeterminate tomato, petiole sap [NO 3(-)-N] was very strongly linearly related to NNI (R(2) = 0.88-0.95, P < 0.001) with very similar slope and intercept values on all dates. Very similar relationships were obtained from published data of processing tomato. A single linear regression (R(2) = 0.77, P < 0.001) described the relationship between sap [NO 3(-)-N] and NNI for both indeterminate and processing tomato, each grown under very different conditions. A single sap [NO 3(-)-N] sufficiency value of 1050 mg N L(-1) was subsequently derived for optimal crop N nutrition (at NNI = 1) of tomato grown under different conditions. In muskmelon, petiole sap [NO 3(-)-N] was strongly linearly related to NNI (R(2) = 0.75 - 0.88, P < 0.001) with very similar slope and intercept values for much of the crop (44-72 DAT, days after transplanting). A single linear relationship between sap [NO 3(-)-N] and NNI (R(2) = 0.77, P < 0.001) was derived for this period, but sap sufficiency values could not be derived for muskmelon as NNI values were >1. Relationships between petiole sap [NO 3(-)-N] with crop N content, and leaf N content with both NNI and crop N content had variable slopes and intercept values during the indeterminate tomato and the muskmelon crops. Soil solution [NO 3(-)] in the root zone was not a sensitive indicator of crop N status. Of the three systems examined for monitoring crop/soil N status, petiole sap [NO 3(-)-N] is suggested to be the most useful because of its sensitivity to crop N status and because it can be rapidly analysed on the farm.

Dataset Information

Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.

Publications

Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.

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