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Copyright of Journal of Northern Agriculture / Beifang Nongye Xuebao is the property of Journal of Northern Agriculture Editorial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current measurement of soil properties can be limited in the variable-rate fertilizer application using soil analysis, due to the labor-intensive and time-consuming in the laboratory. Rapid, non-destructive soil detection is highly required to be developed using visible-infrared spectroscopy. Among them, Raman spectroscopy can be used to detect the vibrational properties of molecules, due to the fast, non-invasive, small sample treatment, and free interference from water in various fields. Nevertheless, incident light is also required to induce the Raman scattering, in order to compensate for the information with infrared spectroscopy. Therefore, the Raman scattering measurement can be widely used in the composition and dynamic process of soil minerals, bacteria colonies, and humic fraction in soil sensing. However, it is still lacking on the fluorescence effect of soil in the sensing of soil nutrients, which can weaken the Raman signals and the information extraction for the quantitative analysis. Raman spectroscopy has been introduced into some novel measurement techniques to improve the signal-to-noise ratio in recent years, including the shifted excitation Raman difference spectroscopy (SERDS), confocal Raman microscope, and surface-enhanced Raman spectroscopy (SERS). Meanwhile, both aliphatic and aromatic compounds are Raman active in the quantitative and qualitative detection of soil organic matter (SOM). Moreover, many phosphorous compounds in soil are also Raman active, leading to low prediction accuracy using visible-near infrared spectroscopy. In this review, the research progress was proposed on the Raman spectroscopy in the rapid detection of soil nutrients, together with the technical means in suppressing soil fluorescence interference to obtain high-resolution Raman signals. In the detection of SOM, the fusion of infrared and Raman spectral data significantly improved the prediction accuracy of 43% in the root mean square error (RMSE). In addition, the SERDS technique was used to detect the SOM of 33 soil samples. The reconstructed Raman peaks of soil minerals and organic materials obtained an excellent prediction accuracy of the SOM, with the determination of coefficient R 2=0.82, and the residual prediction deviation RPD=1.81. Raman spectroscopy was used to detect the water-soluble nitrogen in the soil solution, whereas, the SERS was to enhance the Raman peaks. An excellent correlation was obtained between the concentration of water-soluble nitrogen and the SERS data (R²=0.91). Research showed that Raman spectroscopy can be an effective tool to identify the different phosphate species in soil. Wavelet packet decomposition of Raman spectra was used to predict the phosphorus concentration in the phosphate-mixed soil with the SOM leached, where the accuracy of the regression model reached R²=0.94. Since the measurement area of Raman spectroscopy depended mainly on the spot size of the laser that irradiated on the sample surface, the spatial variability of the soil sample can be difficult to focus on the target substance. The effective Raman signal of soil nutrients can be obtained with high spatial resolution while suppressing the interferences from the background light. The combination of SERDS and micro-Raman technologies can be expected to serve as an in-situ measurement of soil nutrients. Reducing the redundant variables is crucial when applying spectral fusion since interpretability and reproducibility of the prediction model are paramount in the sensor development using Raman spectroscopy. [ABSTRACT FROM AUTHOR]

Smart farming for field crops is a significant part of the smart agriculture. It aims at crop production, integrating mod‐ ern sensing technology, new generation mobile communication technology, computer and network technology, Internet of Things(IoT), big data, cloud computing, blockchain and expert wisdom and knowledge. Deeply integrated application of bio‐ technology, engineering technology, information technology and management technology, it realizes accurate perception, quanti‐ tative decision-making, intelligent operation and intelligent service in the process of crop production, to significantly improve land output, resource utilization and labor productivity, comprehensively improves the quality, and promotes efficiency of agri‐ cultural products. In order to promote the sustainable development of the smart farming, through the analysis of the develop‐ ment process of smart agriculture, the overall objectives and key tasks of the development strategy were clarified, the key tech‐ nologies in smart farming were condensed. Analysis and breakthrough of smart farming key technologies were crucial to the in‐ dustrial development strategy. The main problems of the smart farming for field crops include: the lack of in-situ accurate mea‐ surement technology and special agricultural sensors, the large difference between crop model and actual production, the instan‐ taneity, reliability, universality, and stability of the information transmission technologies, and the combination of intelligent ag‐ ricultural equipment with agronomy. Based on the above analysis, five primary technologies and eighteen corresponding second‐ ary technologies of smart farming for field crops were proposed, including: sensing technologies of environmental and biologi‐ cal information in field, agricultural IoT technologies and mobile internet, cloud computing and cloud service technologies in agriculture, big data analysis and decision-making technology in agriculture, and intelligent agricultural machinery and agricul‐ tural robots in fireld production. According to the characteristics of China's cropping region, the corresponding smart farming development strategies were proposed: large-scale smart production development zone in the Northeast region and Inner Mon‐ golia region, smart urban agriculture and water-saving agriculture development zone in the region of Beijing, Tianjin, Hebei and Shandong, large-scale smart farming of cotton and smart dry farming green development comprehensive test zone in the North‐ west arid region, smart farming of rice comprehensive development test zone in the Southeast coast region, and characteristic smart farming development zone in the Southwest mountain region. Finally, the suggestions were given from the perspective of infrastructure, key technology, talent and policy. [ABSTRACT FROM AUTHOR]

Wheat basic seedling number is one of the most important sources of the total number of wheat ears. In turn, the leading factor can also dominate the wheat yield per unit area. It is an essential prerequisite for the timely and accurate acquisition of the within-field spatial difference information of the wheat basic seedling number. The variable-rate topdressing of nitrogen fertilizer can then be implemented in the manner of precision agriculture. The population density of wheat tillers can often be regulated to realize the fertilizer reduction with a better yield. Unmanned Aerial Vehicle (UAV) remote sensing imagery can be efficiently obtained at the field level in recent years. However, the vegetation and background features can be only processed without considering the influence of mixed pixels of the imagery in the traditional agricultural UAV remote sensing applications. The accuracy and reliability of wheat basic seedling number inversion cannot fully meet the large-scale production in smart agriculture. In this study, the quantitative inversion accuracy of wheat basic seedling numbers was improved using the mixed pixel decomposition model of UAV remote sensing imagery. Firstly, the UAV remote sensing imagery was acquired with a spatial resolution of about 2.5 cm using DJI Mini drone. The relative radiometric calibration was then completed using the invariant target method. Furthermore, the endmembers of vegetation and soil, as well as the mixed pixels were extracted from the reflectance image, which accounted for 2.23%, 0.28%, and 97.49% of the pixels, respectively. The spectral signatures were acquired for the endmembers of vegetation and soil using the reflectance values. Consequently, the decomposition model was established using mixed pixels of UAV remote-sensing images. The linear decomposition was used to divide each mixed pixel into 2 components of vegetation and soil. The abundance data was acquired for each component. The vegetation abundance model was used to calculate the Fractional Vegetation Coverage (FVC) of the experimental field. The proportions of vegetation endmember and abundance were then evaluated over the total area of “1m and 2 rows”. Finally, a linear regression model was established between the FVC and the ground truth data of 15 sets of wheat basic seedling numbers. The determination coefficient R² was 0.87. Besides, the regression model was verified using 3 other ground truth data of wheat basic seedling numbers. The verification results show that the Root Mean Square Error (RMSE) was 1.97 seedlings/m² . The higher inversing accuracy was achieved in this case, compared with the average wheat basic seedling number of 217.442 seedlings/m² for the wheat field. A comparative experiment was performed on the FVC thematic maps. The traditional vegetation index method was used, including the Visible-band Difference Vegetation Index (VDVI), Green Red Difference Index (GRDI), and Green Red Ratio Index (GRRI). The linear regression models were then established between each FVC of VDVI, GRDI, GRRI, and ground truth data of wheat basic seedling number. The determination coefficient R² and RMSE were calculated as 0.79, 0.56, 0.47, and 6.06, 7.04 and 4.43 seedlings/m2, respectively. Therefore, better performance was achieved in the quantitative inversion model of the wheat basic seedling number using the mixed pixels decomposition of UAV remote sensing images. The findings can provide data support for the precise variable topdressing of nitrogen fertilizer at the tillering stage of wheat. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water is essential for plant growth, and water shortage will have an impact on plant yield, growth and quality, therefore, rapid and nondestructive detection of water content in maize leaves is of great significance for scientific guidance of irrigation, and it is also important for improving crop yield. In order to rapidly detect the moisture content of maize leaves, transmission spectrum on the band of 300-1 700 nm was selected to predict moisture content of maize leaves. The testing system included the light source and sensors. The light source part was provided by Wuling Optical Instrument Company. The transmission spectral range of 350-820 nm was obtained with ocean STS-VIS spectrometer, and the spectrum of 900-1 700 nm was measured by Wuling NIRez near-infrared spectrometer. Although the 820-900 nm transmission spectrum was absent, the transmittance curves obtained from different water gradients showed the same trend. Ten maize plants at stage V9 were detected. In order to eliminate the interference of different blade thickness on the experimental results, the first piece of fully expanded leaves from the top of the plant was selected as the experimental sample. The samples were taken back to the laboratory, and 10 cm long strips were cut along the veins from the middle of the leaves. The experiment measured and recorded transmission spectra curve and the moisture content of maize leaves under different water gradient. The moisture content measured by fresh weight moisture content formula, the transmission rate calculated according to Lambert-Beer law. The total number of experimental samples was 98, validation set was 33, and modeling set was 65. The total moisture content of maize leaves was 10%-85% and the average moisture content was 57.9%. In order to eliminate the influence of the noise caused by the spectrometer itself, the Savitzky-Golay method was used for pretreatment. The data was processed using correlation analysis between the transmission spectra and the moisture content, and principal co. As a result, the sensitive wavelengths at 800, 932 and 1 423 nm were selected by the correlation analysis, and the sensitive wavelengths at 478, 748, 1058 and 1323 nm were extracted by principal component analysis. To further improve the determination ability of the model, four sensitive wavelengths were selected, which were 800, 1 323, 1 058 and 1 423 nm. Using the combination of these four wavelengths, 12 vegetation index such as ratio vegetation index, difference vegetation index and normalized difference vegetation index were obtained. In the 12 vegetation indices, the modeling accuracy and prediction accuracy were significantly higher than other vegetation indices, so the DVI (1 423, 800) and DVI (1 423, 1 503) were respectively used to predict the moisture content. The results showed that DVI, which was derived from the combination of strong water absorption band (1 423 nm) and weak water absorption wavelength (800 and 1503 nm), could effectively restrain the disturbance caused by structural changes, and it could sensitively reflect the moisture content of maize leaves. Multivariate linear regression model was established based on DVI (1 423, 800), T1323 and T1058, the calibration R2 reached to 0.968 8, the validation R2 reached to 0.951 9, and the root mean square error of prediction was 0.061. The results showed that water prediction model established by transmission spectroscopy could provide efficient guidance for plant leaf water rapid detecting instrument development. [ABSTRACT FROM AUTHOR]

Accurate prediction of winter wheat chlorophyll content at seedling stage is important for guiding precision management in the field. In order to acquire chlorophyll content of winter wheat leaves, traditional detection methods require to squash winter wheat leaves and applying chemical methods, which would have bad influence on crops growth and cause unnecessary waste of time on some level. It is proved that the spectroscopy analysis is an effective method to predict chlorophyll content of winter wheat. However, the drift and offset of spectral baseline has a great influence on the predicting accuracy. So, this study was carried out to eliminate the influence of the drift and offset. The experimental farm was randomly divided into 70 different sampling areas in Xiaotangshan, Beijing, and the winter wheat leaves were collected on April 20th in the period of seedling stage. The visible and near infrared canopy spectral reflectance of winter wheat was measured by an ASD FieldSpec handheld spectroradiometer at seedling stage. The chlorophyll contents of sampling leaves were detected by the spectrophotometer in the laboratory on the same day. The obtained data of the canopy spectral reflectance and chlorophyll content were assembled for each region individually. The multiple scattering correction (MSC) was used on the bands of 325-1025 nm wavelength, because many scattering errors were introduced into the measured spectral data due to the physical factors. The MSC method first requires establishing an ideal spectrum of all samples, and modifying all the other samples of near infrared spectra on the basis of ideal spectrum to, and spectral reflectance changes with the content of chlorophyll components in the sample meet the direct linear relationship. The absolute intensity difference of spectral reflectance of winter wheat canopy was weakened after the MSC pretreatment, and then scattering effect was effectively reduced. Baseline shift and offset problems were resolved, and the correlation coefficients of spectral reflectance and chlorophyll content were increasing through the MSC pretreatment. Furthermore, genetic algorithm (GA) was proposed for sensitive band selection. GA is a high efficient and globally random search optimization method which simulates Darwin's evolution by natural selection and genetic mechanism of biological evolution. According to the principles of choosing high frequency bands as characteristic wavelength, 486, 599, 699 and 762 nm crop canopy reflectances were selected to calculate vegetation indices, including ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The correlation between each vegetation index and chlorophyll content of winter wheat was analyzed. It was found that the correlation between each vegetation index and chlorophyll content of winter wheat significantly increased after the MSC. The results showed that DVI and SAVI could refrain interference of soil background during seedling period, and the optimal parameters were DVI(762, 599), SAVI(762, 599), DVI(762, 699) and SAVI(762, 699), and the correlation coefficients were all above 0.6. The DVI(762, 699) and SAVI(762, 599) were selected to establish the multiple linear regression (MLR) prediction model and the least squares-support vector regression (LS-SVR) prediction model. The 70 winter wheat samples were divided into 2 groups, 50 samples for model calibration and the other 20 samples for model verification. The results of MLR showed the determination coefficient of the calibration model was 0.528 and that of the validation model was 0.487. In order to improve the precision of the forecast model, the LS-SVR prediction model was applied, and the determination coefficient of the calibration model was 0.681 and that of the validation model was 0.611. It showed that the fitting result was ideal. With the application of spectral technology, it provides a feasible method to detect the winter wheat growth status at seedling stage. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)