Unmanned aerial vehicle (UAV) based active canopy sensors can serve as a promising sensing solution for the estimation of crop nitrogen (N) status with great applicability and flexibility. This study was endeavored to determine the feasibility of UAV-based active sensing to monitor the leaf N status of rice (Oryza sativa L.) and to examine the transferability of handheld-based predictive models to UAV-based active sensing. In this three-year multi-locational study, varied N-rates (0–405 kg N ha−1) field experiments were conducted using five rice varieties. Plant samples and sensing data were collected at critical growth stages for growth analysis and monitoring. The portable active canopy sensor RapidSCAN CS-45 with red, red edge, and near infrared wavebands was used in handheld mode and aerial mode on a gimbal under a multi-rotor UAV. The results showed the great potential of UAV-based active sensing for monitoring rice leaf N status. The vegetation index-based regression models were built and evaluated based on Akaike information criterion, Bayesian information criterion and independent validation to predict rice leaf dry matter, leaf area index, and leaf N accumulation. Vegetation indices composed of near-infrared and red edge bands (NDRE or RERVI) acquired at a 1.5 m aviation height had a good performance for the practical application. Future studies are needed on the proper operation mode and means for precision N management with this system.

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Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status.

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.

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Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

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.

Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring

Portable spectrometers can extract crop growth information in a non-destructive, rapid, and efficient way. However, most existing instruments can only be used to collect spectral reflectance and calculate vegetation indices. They are costly and lack crop spectral monitoring models and diagnosis models; therefore, crop growth information cannot be directly output from these instruments. In this paper, an active light source apparatus for crop growth monitoring and diagnosis (CGMD) was developed based on the canopy shape characteristics and spectral monitoring mechanisms of row-cultivated crops. A modulated light source was used in this apparatus to effectively eliminate the interference of ambient light. At the same time, a high-pass filter was used in the apparatus to extract the canopy reflectance spectral information, which improves the signa-noise ratio. Crop canopy vegetation indexes—the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI)—and crop growth parameters—leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen accumulation (LNA), and leaf nitrogen content (LNC)—were obtained in real time. The ambient light influence test and the comparison test of different test heights of the CGMD apparatus show that it has high test stability and can effectively overcome ambient light interference within the standard test height range. The rice and wheat experimental results demonstrated that the test results of the CGMD apparatus and the commercial Analytical Spectral Devices field spectrometer (Malvern Panalytical, Malvern, Worcs, UK) were consistent. Comparing the vegetation indexes and agronomic parameters obtained from the chemical analysis, the CGMD apparatus has a good predictive ability for LAI, LDW, LNC, and LNA.

Design and Testing of an Active Light Source Apparatus for Crop Growth Monitoring and Diagnosis

The embodiment of the invention provides a growth monitoring model updating method, a sensor, a server and a system. The method comprises the following steps: collecting spectral data of crops; acquiring growth parameter data of the crops; sending the spectral data and the growth parameter data to a server to ensure that the server extracts effective spectral data and effective growth parameter data in different crop type distribution areas according to the spectral data, the growth parameter data of the crops and a crop planting distribution map, inputs the effective spectral data and the effective growth parameter data into a neural network for machine learning, and obtains a mapping relationship between the spectral data and the growth parameter data of the crops; and receiving the mapping relationship sent by the server. According to the growth monitoring model updating method, the sensor, the server and the system, provided by the embodiment of the invention, the sharing of data observed by multiple sensors and the dynamic update of models in the sensors can be achieved, and the universality and accuracy of the models can be improved.

Growth monitoring model updating method, sensor, server and system

The invention discloses a crop growth monitoring device and method based on a vehicle-mounted system. The crop growth monitoring device based on the vehicle-mounted system is characterized by comprising a multi-spectrum crop growth sensor, a spectral data collector, a GPS base station, a GPS receiver, a vehicle-mounted system speed sensor, an acceleration sensor, a gravity sensor, an electronic compass, a vehicle-mounted system status collector, a shielding cable, a vehicle-mounted terminal, a sensor support and a power circuit. The crop growth monitoring device and method based on the vehicle-mounted system integrates attribute information quantity measured by crop reflection spectrums, monitoring of vector information, spectral information correction, reflection spectrum local imaging display and real-time dynamic display of a decision model and has the advantages of being high in measuring accuracy, capable of carrying out real-time continuous measurement, large in information quantity, high in processing speed and the like. Timely and effective data acquisition provides possibility and convenience for large-area implementation of precision farming, and the problems in existing crop growth monitoring are solved.

Crop growth monitoring device and method based on vehicle-mounted system

Purpose – The purpose of this paper is to improve the accuracy and signal-to-noise ratio (SN) of a crop nitrogen sensor. Design/methodology/approach – The accuracy and wide adaptability of two spectral calibration methods for a crop nitrogen sensor based on standard reflectivity gray plates and standard detector, respectively, were compared. Findings – The calibration method based on standard detector could significantly improve the measurement accuracy and the SN of this crop nitrogen sensor. When compared with the method based on standard gray plates, the measurement accuracy and the SN of the crop nitrogen sensor calibrated based on the standard detector method improved by 50 and 10 per cent, respectively. Originality/value – This research analysed the calibration problems faced by the crop nitrogen sensor (type CGMD302) based on standard gray plates, and proposed a sensor calibration method based on a standard detector. Finally, the results of the two calibration methods were compared in terms of meas...

The spectral calibration method for a crop nitrogen sensor

The invention discloses a method for building a temperature compensation model of a multispectral crop growth sensor The method can realize temperature compensation of reflectivity by carrying out temperature compensation on output voltage of the sensor The method is based on temperature and at the same time is suitable for building prediction models of output voltages of a 720 nm uplink optical sensor and a 810 nm downlink optical sensor, and obtains the temperature compensation models of the uplink optical sensor and the downlink optical sensor through the prediction models; the reflectivity is calculated according to the output voltage after temperature compensation to realize temperature compensation for the reflectivity of the multispectral crop growth sensor The method builds the low-calculation-amount and high-precision temperature compensation model of the multispectral crop growth sensor, and improves the temperature stability of the sensor during field application, the temperature characteristic of the multispectral crop growth sensor can be understood, the temperature prediction models of the output voltage of the sensors are built, and the temperature compensation of the reflectivity of the sensor is realized

Method for building temperature compensation model of multispectral crop growth sensor

The invention provides a spectral calibration method for a crop nitrogen sensor. The method is characterized by comprising the following steps: 1) respectively calibrating the irradiance of an up light sensor and the radiance of a down light sensor by using a standard detector, i.e., in virtue of the up light sensor and the standard detector, synchronously measuring the irradiance of the waveband r of sun light and fitting a linear relation between the output DN_Up(r) of the up light sensor and the output EB(r) of the standard detector so as to realize calibration of the optical radiance of the up light sensor as shown in a formula (3) which is described in the specification; etc. The up light sensor is in good linear relation with the irradiance of sun light, the down light sensor is in good linear relation with the radiance, and linear relativity reaches 99%. The calibration method based on radiation can greatly improve measurement precision and stability of the crop nitrogen sensor compared with a calibration method based on a standard gray scale plate.

Dong Jifei

Papers

Crop growth monitoring device and method based on vehicle-mounted system

The spectral calibration method for a crop nitrogen sensor

Method for building temperature compensation model of multispectral crop growth sensor

Spectral calibration method for crop nitrogen sensor