Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use

Evaluation of optical sensor technologies to optimize winter wheat (Triticum aestivum L.) management

Master of ScienceDepartment of AgronomyAntonio R. AsebedoSensor technology has become more important in precision agriculture, by real time sensing for site specific management to monitor crops during the season especially nitrogen (N). In Kansas N available in the soils can vary year to year or over a course of a year. The objective of this study was to compare current available passive (PS) and active optical sensor technologies (AOS) performance in regards to sky conditions effects and derive the NDVI (normalized difference vegetation index) relationship to wheat yield, as well as evaluate KSU optical sensor-based N recommendations against KSU soil test N recommendation system and sUAS (small unmanned aircraft systems) based recommendation algorithms with the PS and AOS platforms. Each year (2015-2016 & 2016-2017) five field trails across Kansas were conducted during the winter wheat crop year in cooperation with county ag agents, farmers, and KSU Agronomy Experiment Fields. Treatments consisted of N response curve, 1st and 2nd generation KSU N recommendation algorithms, sUAS based recommendation algorithms, and KSU soil test based N recommendations applied in the spring using N rates ranging from 0 to 140 kg ha⁻¹. Results indicate the Holland Scientific Rapid Scan and MicaSense RedEdge NDVI data was strongly correlated and generated strong relationships with grain yield at 0.60 and 0.57 R² respectively. DJI X3 lacks an NIR band producing uncalibrated false NDVI and no relationship to grain yield at 0.03 R². Calibrated NDVI from both sensors are effective for assessing yield potential and could be utilized for developing N recommendation algorithms. However, sensor based treatments preformed equal to higher yields compared the KSU soil test recommendations, as well as reduced the amount of fertilizer applied compared to the soil test recommendation. The intensive management algorithm was the most effective in determining appropriate N recommendations across locations. This allows farmers to take advantage of potential N mineralization that can occur in the spring. Further research is needed considering on setting the NUE (nitrogen use efficiency) in KSU N rec. algorithms for effects of management practice, weather, and grain protein for continued refinement

NITROGEN AND WATER EFFECTS ON CANOPY SENSOR MEASUREMENTS FOR SITE-SPECIFIC MANAGEMENT OF CROPS

Water and nitrogen (N) are undoubtedly the two largest agricultural inputs globally. Coupled with advances in site-specific management technology their integration into production agriculture will allow for the most efficient use these crop input resources. Active canopy sensors offer the ability to measure biophysical plant traits rapidly and make assessments about plant status. Specifically, optical sensor measurements of light reflectance assess plant N status allowing for in-season and on-the-go N recommendations and applications; while infrared thermometers (IRT) measurement of canopy temperature can be used a tool for irrigation management. To evaluate how these technologies work among different plant stress environments a series of experiments were formulated. The first experiment compared reference strategies for normalizing reflectance data across multiple vegetation indices (VI). We found the virtual reference concept helped reduce variation of the calculated reference and placed sufficiency index values in a range that corresponded to plant N status. Additionally, VI varied in their ability to show significant responses to applied N fertilizer. In the second experiment, we sought to understand the influence of VI on how an in-season N application algorithm performs as well as the confounding effects of irrigation might have. We found N application rates would change based on algorithm and VI. Also, N rate can be affected by apparent water stress. In this case, reduced reflectance in the NIR spectrum reduced leaf area from leaf rolling. The final objective was to quantify the effect of N fertility on plant canopy temperature and determine if functions of canopy temperature could be useful for detecting apparent N stress. We concluded that plant canopy temperature can be affected by N stresses and that canopy temperature and canopy/air temperature difference provided equal sensitivity to plant stress. Therefore, these technologies will be vital to help conserve resources and maximize efficiency in production agriculture. Advisor: Richard Ferguso

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

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 3-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 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

Recent advances in chemical sensors for soil analysis: a review

The continuously rising interest in chemical sensors' applications in environmental monitoring, for soil analysis in particular, is owed to the sufficient sensitivity and selectivity of these analytical devices, their low costs, their simple measurement setups, and the possibility to perform online and in-field analyses with them. In this review the recent advances in chemical sensors for soil analysis are summarized. The working principles of chemical sensors involved in soil analysis; their benefits and drawbacks; and select applications of both the single selective sensors and multisensor systems for assessments of main plant nutrition components, pollutants, and other important soil parameters (pH, moisture content, salinity, exhaled gases, etc.) of the past two decades with a focus on the last 5 years (from 2017 to 2021) are overviewed

Crop assessment and monitoring using optical sensors

Doctor of PhilosophyDepartment of AgronomyV. P. Vara PrasadCrop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error

COVER CROPPING: SENSOR-BASED ESTIMATIONS OF BIOMASS YIELD AND NUTRIENT UPTAKE AND ITS IMPACT ON SUGARCANE PRODUCTIVITY

Sugarcane in Louisiana can be harvested for up to three years from one planting. Soil cultivation along sides of established beds is done for weed control and improve fertilizer use efficiency which increases the risk of soil degradation and yield decline. Planting cover crops (CC) is a soil conservation practice and an effective strategy to improve soil health and nutrient recycling. Limited work has been done on remote sensor-based evaluation of the potential nutrient benefits from cover crops and its effect on nutrient cycling on sugarcane systems. This study was conducted to evaluate the effect of two planting methods (broadcast and drilling) and three seeding rates (100%, 50%, and 25% of NRCS recommendation) of a mix of three legumes and two brassicas CC species and a control without CC, on sugarcane yield and quality parameters, and on soil nutrients levels. This study was also used for the acquisition of normalized difference vegetation index (NDVI), collected using GreenSeeker® and multispectral camera (MicaSense® - RedEdge-M) mounted on an unmanned aerial vehicle, to correlate with CC biomass and nutrient uptake. The NDVI readings and CC biomass clippings, using the quadrat frame method, were collected a week before CC termination. Tissue analysis was carried out by C:N dry combustion analyzer and nitric acid digestion-hydrogen peroxide for multi-element analysis. Cane yield was acquired with a chopper harvester and a dump billet wagon. Quality components were obtained by a SpectraCane® automated near infrared (NIR) analyzer for quality parameters. Soil inorganic nitrogen (N) content (NH4+ + NO3-) was quantified using KCl extraction procedure and flow injection analysis. Other soil nutrients content was determined based on Mehlich-3 extraction procedure followed by ICP. A strong positive correlation between the GreenSeeker NDVI (NDVI-GS) and aerial images derived NDVI (NDVI-AI) was obtained with a coefficient of determination (R2) value of 0.63. Adjustment of NDVI with, number of days, cumulative growing degree days, and number of days with positive growing degree days, from planting to sensing increased the R2 values up to 0.76, 0.76 and 0.73, respectively. The NDVI-GS obtain a stronger linear relationship with CC dry biomass and N content than NDVI-AI. Good positive correlations (0.48 \u3e R2 \u3e 0.12) were found between NDVI and some macronutrients (P and K) and micronutrients (Mn and Cu). Overall, there was no significant effect of planting method and seeding rate observed on cane yield and quality parameters. Moreover, there was no statistical difference on CC nutrient removal rate among the treatments (p\u3e0.05). For plant cane, the average cane and sugar yield across sites was 96 Mg ha-1 and 10794 kg ha-1, respectively. Lower yield was attained by the ratoon crops averaging only at 71 Mg ha-1 cane yield and 7197 kg ha-1 sugar yield. Remote sensing is a promising and viable technique to estimate CC biomass and nutrient uptake. Finally, this study corroborates the long-term effect of CC on nutrient management and their effect on cane yield and quality parameters

A real-time plant discrimination system utilising discrete reflectance spectroscopy

An advanced, proof-of-concept real-time plant discrimination system is presented that employs two visible (red) laser diodes (635. nm, 685. nm) and one near-infrared (NIR) laser diode (785. nm). The lasers sequentially illuminate the target ground area and a linear sensor array measures the intensities of the reflected laser beams. The spectral reflectance measurements are then processed by an embedded microcontroller running a discrimination algorithm based on dual Normalised Difference Vegetation Indices (NDVI). Pre-determined plant spectral signatures are used to define unique regions-of-classification for use by the discrimination algorithm. Measured aggregated NDVI values that fall within a region-of-classification (RoC) representing an unwanted plant generate a spray control signal that activates an external spray module, thus allowing for a targeted spraying operation. Dynamic outdoor evaluation of the advanced, proof-of-concept real-time plant discrimination system, using three different plant species and control data determined under static laboratory conditions, shows that the system can perform green-from-green plant detection and accomplish practical discrimination for a vehicle speed of 3. km/h

Satellite and Fluorescence Remote Sensing for Rice Nitrogen Status Diagnosis in Northeast China

Nitrogen (N), as the most important element of crop growth and development, plays a decisive role in ensuring yield. However, the problems of over-application of N fertilizers have been repeatedly reported in China, which resulted in low N use efficiency and high risk of environmental pollution. The requirements of developing technologies for real-time and site-specific diagnosis of crop N status are the foundation to realize the precision N management, and also benefit to the improvement of the N use efficiency. Remote sensing technology provides a promising non-intrusive solution to monitor rice N status and to realize the precision N management over large areas. This research focuses on proposing N nutrition diagnosis methods and developing N fertilizer management strategies for paddy rice of cold regions in Northeast China. The main contents and results are presented as follows: (1)This study developed a new critical N (Nc) dilution curve for paddy rice of cold regions in Northeast China. The curve could be described by the equation Nc=27.7W^(-0.34) if W≥1 t/ha for dry matter (DM) or Nc=27.7g/kg DM if W<1 t/ha, where W is the aboveground biomass. Results indicated that the new Nc dilution curve was suitable for diagnosing short-season Japonica rice N status in Northeast China. The validation result indicated that it worked well to diagnose plant N status of the 11-leaf variety rice. (2)This study investigated the potential of the satellite remote sensing data for diagnosing rice N status and guiding the topdressing N application at the stem elongation stage in Northeast China. 50 vegetation indices (VIs) were computed based on the FORMOSAT-2 satellite data, and they were correlated with the field-based agronomic variables, i.e., aboveground biomass (AGB), leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), chlorophyll meter readings, and N nutrition index (NNI, defined as the ratio of actual PNC and critical PNC according to the new Nc dilution curves). The results presented that 45% of variation in the NNI was obtained by using a direct estimation method based on the best VI according to the FORMOSAT-2 satellite data, while 52% of the variation in the NNI was yielded by an indirect estimation method, which firstly used the VIs to estimate AGB and PNU, respectively, then estimated NNI according to these two variables. Moreover, based on the critical N uptake curve, a N recommendation algorithm was proposed. The algorithm was based on the difference between the estimated PNU and the critical PNU to adjust the topdressing N application rate. The results demonstrated that FORMOSAT-2 images have the potential to estimate rice N status and guide panicle N fertilizer applications in Northeast China. (3)This study also evaluated the potential improvements of the newest satellite sensors with the red edge band for diagnosing rice N status in Northeast China. The canopy-scale hyperspectral data were upscaled to simulate the wavebands of RapidEye, WorldView-2, and FORMOSAT-2, respectively. The VI analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to evaluate the N status indicators. The results indicated that the VIs based on the RE band from RapidEye and WorldView-2 data could explain more variability for N indicators than the VIs from FORMOSAT-2 data having no RE band. Moreover, the SMLR and PLSR results revealed that both the near-infrared and red edge band were important for N status estimation. (4)The proximal fluorescence sensor Multiplex_3 was used to evaluate the potential of fluorescence spectrum for estimating the N status of the cold regional paddy rice at different growth stages. The Multiplex indices and their normalized N sufficient indices (NSI) were used to estimate the five N status indicators, i.e., AGB, leaf N concentration (LNC), PNC, PNU, and NNI. The results indicated that there were strong relationships between the fluorescence indices (i.e., BRR_FRF, FLAV, NBI_G, and NBI_R) and (i.e., LNC, PNC, NNI), with the coefficient of determination between 0.40 and 0.78. In particular, NNI was well estimated by these fluorescence indices. Moreover, the NSI data improved the accuracy of the N diagnosis. These results of this study were useful for N nutrition diagnosis and variable fertilization of the cold regional paddy rice, which were significant for the ecological environment protection and the national food security