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The accurate estimation of nitrogen accumulation is of great significance to nitrogen fertilizer management in wheat production. To overcome the shortcomings of spectral technology, which ignores the anisotropy of canopy structure when predicting the nitrogen accumulation in wheat, resulting in low accuracy and unstable prediction results, we propose a method for predicting wheat nitrogen accumulation based on the fusion of spectral and canopy structure features. After depth images are repaired using a hole-filling algorithm, RGB images and depth images are fused through IHS transformation, and textural features of the fused images are then extracted in order to express the three-dimensional structural information of the canopy. The fused images contain depth information of the canopy, which breaks through the limitation of extracting canopy structure features from a two-dimensional image. By comparing the experimental results of multiple regression analyses and BP neural networks, we found that the characteristics of the canopy structure effectively compensated for the model prediction of nitrogen accumulation based only on spectral characteristics. Our prediction model displayed better accuracy and stability, with prediction accuracy values (R2) based on BP neural network for the leaf layer nitrogen accumulation (LNA) and shoot nitrogen accumulation (SNA) during a full growth period of 0.74 and 0.73, respectively, and corresponding relative root mean square errors (RRMSEs) of 40.13% and 35.73%.
Ke Xu; Jingchao Zhang; Huaimin Li; Weixing Cao; Yan Zhu; Xiaoping Jiang; Jun Ni. Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat. Remote Sensing 2020, 12, 4040 .
AMA StyleKe Xu, Jingchao Zhang, Huaimin Li, Weixing Cao, Yan Zhu, Xiaoping Jiang, Jun Ni. Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat. Remote Sensing. 2020; 12 (24):4040.
Chicago/Turabian StyleKe Xu; Jingchao Zhang; Huaimin Li; Weixing Cao; Yan Zhu; Xiaoping Jiang; Jun Ni. 2020. "Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat." Remote Sensing 12, no. 24: 4040.
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.
Huaimin Li; Weipan Lin; Fangrong Pang; Xiaoping Jiang; Weixing Cao; Yan Zhu; Jun Ni. Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis. Sensors 2020, 20, 2894 .
AMA StyleHuaimin Li, Weipan Lin, Fangrong Pang, Xiaoping Jiang, Weixing Cao, Yan Zhu, Jun Ni. Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis. Sensors. 2020; 20 (10):2894.
Chicago/Turabian StyleHuaimin Li; Weipan Lin; Fangrong Pang; Xiaoping Jiang; Weixing Cao; Yan Zhu; Jun Ni. 2020. "Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis." Sensors 20, no. 10: 2894.
Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.
Lili Yao; Qing Wang; Jinbo Yang; Yu Zhang; Yan Zhu; Weixing Cao; Jun Ni. UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status. Sensors 2019, 19, 816 .
AMA StyleLili Yao, Qing Wang, Jinbo Yang, Yu Zhang, Yan Zhu, Weixing Cao, Jun Ni. UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status. Sensors. 2019; 19 (4):816.
Chicago/Turabian StyleLili Yao; Qing Wang; Jinbo Yang; Yu Zhang; Yan Zhu; Weixing Cao; Jun Ni. 2019. "UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status." Sensors 19, no. 4: 816.
To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.
Jun Ni; Jingchao Zhang; Rusong Wu; Fangrong Pang; Yan Zhu. Development of an Apparatus for Crop-Growth Monitoring and Diagnosis. Sensors 2018, 18, 3129 .
AMA StyleJun Ni, Jingchao Zhang, Rusong Wu, Fangrong Pang, Yan Zhu. Development of an Apparatus for Crop-Growth Monitoring and Diagnosis. Sensors. 2018; 18 (9):3129.
Chicago/Turabian StyleJun Ni; Jingchao Zhang; Rusong Wu; Fangrong Pang; Yan Zhu. 2018. "Development of an Apparatus for Crop-Growth Monitoring and Diagnosis." Sensors 18, no. 9: 3129.
Wireless channel propagation characteristics and models are important to ensure the communication quality of wireless sensor networks in agriculture. Wireless channel attenuation experiments were carried out at different node antenna heights (0.8 m, 1.2 m, 1.6 m, and 2.0 m) in the tillering, jointing, and grain filling stages of rice fields. We studied the path loss variation trends at different transmission distances and analyzed the differences between estimated values and measured values of path loss in a free space model and a two-ray model. Regression analysis of measured path loss values was used to establish a one-slope log-distance model and propose a modified two-slope log-distance model. The attenuation speed in wireless channel propagation in rice fields intensified with rice developmental stage and the transmission range had monotone increases with changes in antenna height. The relative error (RE) of estimation in the free space model and the two-ray model under four heights ranged from 6.48–15.49% and 2.09–13.51%, respectively, and these two models were inadequate for estimating wireless channel path loss in rice fields. The ranges of estimated RE for the one-slope and modified two-slope log-distance models during the three rice developmental stages were 2.40–2.25% and 1.89–1.31%, respectively. The one-slope and modified two-slope log-distance model had better applicability for modeling of wireless channels in rice fields. The estimated RE values for the modified two-slope log-distance model were all less than 2%, which improved the performance of the one-slope log-distance model. This validates that the modified two-slope log-distance model had better applicability in a rice field environment than the other models. These data provide a basis for modeling of sensor network channels and construction of wireless sensor networks in rice fields. Our results will aid in the design of effective rice field WSNs and increase the transmission quality in rice field sensor networks.
Zhenran Gao; Weijing Li; Yan Zhu; Yongchao Tian; Fangrong Pang; Weixing Cao; Jun Ni. Wireless Channel Propagation Characteristics and Modeling Research in Rice Field Sensor Networks. Sensors 2018, 18, 3116 .
AMA StyleZhenran Gao, Weijing Li, Yan Zhu, Yongchao Tian, Fangrong Pang, Weixing Cao, Jun Ni. Wireless Channel Propagation Characteristics and Modeling Research in Rice Field Sensor Networks. Sensors. 2018; 18 (9):3116.
Chicago/Turabian StyleZhenran Gao; Weijing Li; Yan Zhu; Yongchao Tian; Fangrong Pang; Weixing Cao; Jun Ni. 2018. "Wireless Channel Propagation Characteristics and Modeling Research in Rice Field Sensor Networks." Sensors 18, no. 9: 3116.
To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0–100 cm. According to the calibration results, the degree of fitting (R2) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0–1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R2 of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R2 between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R2 between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for precise agricultural irrigation with stable performance and high accuracy.
Zhenran Gao; Yan Zhu; Cheng Liu; Hongzhou Qian; Weixing Cao; Jun Ni. Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers. Sensors 2018, 18, 1648 .
AMA StyleZhenran Gao, Yan Zhu, Cheng Liu, Hongzhou Qian, Weixing Cao, Jun Ni. Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers. Sensors. 2018; 18 (5):1648.
Chicago/Turabian StyleZhenran Gao; Yan Zhu; Cheng Liu; Hongzhou Qian; Weixing Cao; Jun Ni. 2018. "Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers." Sensors 18, no. 5: 1648.
In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.
Jun Ni; Lili Yao; Jingchao Zhang; Weixing Cao; Yan Zhu; Xiuxiang Tai. Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System. Sensors 2017, 17, 502 .
AMA StyleJun Ni, Lili Yao, Jingchao Zhang, Weixing Cao, Yan Zhu, Xiuxiang Tai. Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System. Sensors. 2017; 17 (3):502.
Chicago/Turabian StyleJun Ni; Lili Yao; Jingchao Zhang; Weixing Cao; Yan Zhu; Xiuxiang Tai. 2017. "Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System." Sensors 17, no. 3: 502.
Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change.
Naisen Liu; Weixing Cao; Yan Zhu; Jingchao Zhang; Fangrong Pang; Jun Ni. Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring. Sensors 2016, 16, 2096 .
AMA StyleNaisen Liu, Weixing Cao, Yan Zhu, Jingchao Zhang, Fangrong Pang, Jun Ni. Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring. Sensors. 2016; 16 (12):2096.
Chicago/Turabian StyleNaisen Liu; Weixing Cao; Yan Zhu; Jingchao Zhang; Fangrong Pang; Jun Ni. 2016. "Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring." Sensors 16, no. 12: 2096.
Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.
Naisen Liu; Weixing Cao; Yan Zhu; Jingchao Zhang; Fangrong Pang; Jun Ni. The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil. Sensors 2015, 15, 28314 -28339.
AMA StyleNaisen Liu, Weixing Cao, Yan Zhu, Jingchao Zhang, Fangrong Pang, Jun Ni. The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil. Sensors. 2015; 15 (11):28314-28339.
Chicago/Turabian StyleNaisen Liu; Weixing Cao; Yan Zhu; Jingchao Zhang; Fangrong Pang; Jun Ni. 2015. "The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil." Sensors 15, no. 11: 28314-28339.
Various sensors have been used to obtain the canopy spectral reflectance for monitoring above-ground plant nitrogen (N) uptake in winter wheat. Comparison and intercalibration of spectral reflectance and vegetation indices derived from different sensors are important for multi-sensor data fusion and utilization. In this study, the spectral reflectance and its derived vegetation indices from three ground-based sensors (ASD Field Spec Pro spectrometer, CropScan MSR 16 and GreenSeeker RT 100) in six winter wheat field experiments were compared. Then, the best sensor (ASD) and its normalized difference vegetation index (NDVI (807, 736)) for estimating above-ground plant N uptake were determined (R2 of 0.885 and RMSE of 1.440 g·N·m−2 for model calibration). In order to better utilize the spectral reflectance from the three sensors, intercalibration models for vegetation indices based on different sensors were developed. The results indicated that the vegetation indices from different sensors could be intercalibrated, which should promote application of data fusion and make monitoring of above-ground plant N uptake more precise and accurate.
Xinfeng Yao; Xia Yao; Wenqing Jia; Yongchao Tian; Jun Ni; Weixing Cao; Yan Zhu. Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat. Sensors 2013, 13, 3109 -3130.
AMA StyleXinfeng Yao, Xia Yao, Wenqing Jia, Yongchao Tian, Jun Ni, Weixing Cao, Yan Zhu. Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat. Sensors. 2013; 13 (3):3109-3130.
Chicago/Turabian StyleXinfeng Yao; Xia Yao; Wenqing Jia; Yongchao Tian; Jun Ni; Weixing Cao; Yan Zhu. 2013. "Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat." Sensors 13, no. 3: 3109-3130.