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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.)