Leaf nitrogen determination using non-destructive techniques–A review

© 2017 Taylor & Francis Group, LLC. The optimisation of plant nitrogen-use-efficiency (NUE) has a direct impact on increasing crop production by optimising use of nitrogen fertiliser. Moreover, it protects environment from negative effects of nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture systems has been major focus of many researchers. Improvement of NUE can be achieved through several methods including more accurate measurement of foliar N contents of crops during different growth phases. There are two types of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and time-consuming, as they require tissue sampling and subsequent laboratory analysis. Thus, many farmers find destructive methods to be less attractive. Non-destructive methods are rapid and less expensive but are usually less accurate. Accordingly, improving the accuracy of non-destructive N estimations has become a common goal of many researchers, and various methods varying in complexity and optimality have been proposed for this purpose. This paper reviews various commonly used non-destructive methods for estimating foliar N status of plants

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Effects of field inoculation with VAM and bacteria consortia on root growth and nutrients uptake in common wheat

This study investigated the effects of a commercial biofertilizer containing the mycorrhizal fungus Rhizophagus irregularis and the diazotrophic N-fixing bacterium Azotobacter vinelandii on root and shoot growth, yield, and nutrient uptake in common wheat (Triticum aestivum L.) in order to improve the sustainable cultivation of this widespread crop. The trials were carried out in controlled conditions (rhizoboxes) and in open fields over two years to investigate the interaction between inoculation and three doses of nitrogen fertilization (160, 120 and 80 kg ha1) in a silty-loam soil of the Po Plain (NE Italy). In rhizoboxes, efficient root colonization by R. irregularis was observed at 50 days after sowing with seed inoculation, together with improved root tip density and branching (+~30% vs. controls), while the effects of post-emergence inoculation by soil and foliar spraying were not observable at plant sampling. In the open, field spraying at end tillering significantly increased the volumetric root length density (RLD, +22% vs. controls) and root area density (+18%) after about two months (flowering stage) in both years under medium and high N fertilization doses, but not at the lowest N dose. In absence of inoculation, RLD progressively decreased with increased N doses. Inoculation had a negligible effect on grain yield and N uptake, which followed a typical N dose-response model, while straw Zn, P, and K concentrations were seldom improved. It is concluded that medium-high N fertilization doses are required to achieve the target yield and standards of quality (protein contents) in wheat cultivation, while the use of this mixed VAM-PGPR biofertilizer appears to be a sustainable mean for minimizing the adverse effects of chemical N fertilizers on root expansion and for improving the uptake of low-mobility nutrients, which has potentially relevant environmental benefits

Remote sensing and machine learning for prediction of wheat growth in precision agriculture applications

This thesis focuses on remote sensing and machine learning for prediction of wheat growth in precision agriculture applications. Agriculture is the primary productive force, which plays an important role in human activities. Wheat, as one of the essential sources of food, is also a widely planted crop. The impact of weather and climate and some other uncertain factors on wheat production is crucial. Therefore, it is necessary to use reliable and statistically reasonable models for crop growth and yield prediction based on vegetation index variables and other factors, so as to obtain reliable prediction for efficient production. Applying certain artificial intelligence algorithms to the precision agriculture can significantly improve the efficiency of traditional agriculture in crop planting and reduce the consumption of human and natural resources. Remote sensing can objectively, accurately and timely provide a large amount of information for ecological environment and crop growth in agriculture applications. By combining the image and spectral data obtained by remote sensing technology with machine learning, information about wheat growth, yield and insect pests can be learned in time. This thesis focuses on its applications in agriculture, particularly using effective prediction models such as the back propagation neural network and some optimisation algorithms for predicting wheat growth, yield and aphid. The work presented in this thesis address the issues of wheat growth prediction, yield assessment and aphid validation by model building and machine learning algorithm optimisation by means of remote sensing data. Specifically, the following objectives are defined: 1. Analyse multiple vegetation indexes based on the TM 1-4 band data of Landsat satellite and use regression algorithms to train the models and predict wheat growth; 2. Analyse and compare multiple vegetation indexes models by means of spectral data and use regression algorithms to predict wheat yield; 3. Combine spectral vegetation indexes and multiple regression algorithms to predict wheat aphid; 4. Use accurate evaluation criteria for validating the efficacy of the various algorithms. In this thesis, the remote sensing data from the satellite has been applied instead of the airborne-based remote sensing data. Based on the TM 1-4 band image data of Landsat satellite, multiple vegetation indexes were used as the input of regression algorithms. After that, four kinds of regression algorithms such as the multiple linear regression (MR) algorithm, back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm were used to train the model and predict the LAI and SPAD. The prediction results of each algorithm were compared with the ground truth information collected by hand held instruments on the ground. The relationship between wheat yield and spectral data has been studied. Based on the BPNN algorithm, four kinds of models such as visible hyperspectral index (VHI) model, hyperspectral vegetation index (HVI) model, difference hyperspectral index (DHI) model and normalized hyperspectral index (NHI) model have been utilized to predict wheat yield. For the optimal NHI model, three regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm, were compared to predict wheat yield, and RMSE and R-square of the three algorithms were compared and analysed. Finally, the relationship between wheat aphid and spectral data has been investigated. Nine vegetation indexes related to aphid have been estimated from spectral data as the input of regression algorithms. Five kinds of regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm, particle swarm optimisation (PSO) optimised BPNN algorithm, ant colony (ACO) optimisation algorithm optimised BPNN algorithm and cuckoo search (CS) optimised BPNN algorithm have been implemented to predict wheat aphid, which was validated with the ground truth information measured by hand-held instruments on the ground. The prediction results of each algorithm have been analysed. The major original contributions of this thesis are as follows: 1. A variety of optimisation algorithms are used to improve the regression analysis of the BPNN algorithm, so that the prediction results of each model for wheat growth, yield and aphid are more accurate. 2. The spectral characteristics of winter wheat canopy have been analysed. The correlation between the absorption band and the associated physical and chemical properties of crops, specially the red edge slope, with the crop yield and wheat aphid damage is established. 3. Adjusted MSE and un-centered R-square, as accurate evaluation criteria for practical applications, are used to compare the prediction results of the models under different dimensions of the observed data. 4. Improve algorithm training by using the cross-validation method to obtain reliable and stable models for the prediction of wheat growth, yield, and aphid. Through repeated cross-validation, a better model can be obtained in the last. Key word：Precision agriculture; BP network, wheat growth assessment; wheat yield prediction, wheat aphid validationThis thesis focuses on remote sensing and machine learning for prediction of wheat growth in precision agriculture applications. Agriculture is the primary productive force, which plays an important role in human activities. Wheat, as one of the essential sources of food, is also a widely planted crop. The impact of weather and climate and some other uncertain factors on wheat production is crucial. Therefore, it is necessary to use reliable and statistically reasonable models for crop growth and yield prediction based on vegetation index variables and other factors, so as to obtain reliable prediction for efficient production. Applying certain artificial intelligence algorithms to the precision agriculture can significantly improve the efficiency of traditional agriculture in crop planting and reduce the consumption of human and natural resources. Remote sensing can objectively, accurately and timely provide a large amount of information for ecological environment and crop growth in agriculture applications. By combining the image and spectral data obtained by remote sensing technology with machine learning, information about wheat growth, yield and insect pests can be learned in time. This thesis focuses on its applications in agriculture, particularly using effective prediction models such as the back propagation neural network and some optimisation algorithms for predicting wheat growth, yield and aphid. The work presented in this thesis address the issues of wheat growth prediction, yield assessment and aphid validation by model building and machine learning algorithm optimisation by means of remote sensing data. Specifically, the following objectives are defined: 1. Analyse multiple vegetation indexes based on the TM 1-4 band data of Landsat satellite and use regression algorithms to train the models and predict wheat growth; 2. Analyse and compare multiple vegetation indexes models by means of spectral data and use regression algorithms to predict wheat yield; 3. Combine spectral vegetation indexes and multiple regression algorithms to predict wheat aphid; 4. Use accurate evaluation criteria for validating the efficacy of the various algorithms. In this thesis, the remote sensing data from the satellite has been applied instead of the airborne-based remote sensing data. Based on the TM 1-4 band image data of Landsat satellite, multiple vegetation indexes were used as the input of regression algorithms. After that, four kinds of regression algorithms such as the multiple linear regression (MR) algorithm, back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm were used to train the model and predict the LAI and SPAD. The prediction results of each algorithm were compared with the ground truth information collected by hand held instruments on the ground. The relationship between wheat yield and spectral data has been studied. Based on the BPNN algorithm, four kinds of models such as visible hyperspectral index (VHI) model, hyperspectral vegetation index (HVI) model, difference hyperspectral index (DHI) model and normalized hyperspectral index (NHI) model have been utilized to predict wheat yield. For the optimal NHI model, three regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm and particle swarm optimisation (PSO) optimised BPNN algorithm, were compared to predict wheat yield, and RMSE and R-square of the three algorithms were compared and analysed. Finally, the relationship between wheat aphid and spectral data has been investigated. Nine vegetation indexes related to aphid have been estimated from spectral data as the input of regression algorithms. Five kinds of regression algorithms such as back propagation network (BPNN) algorithm, genetic algorithm (GA) optimised BPNN algorithm, particle swarm optimisation (PSO) optimised BPNN algorithm, ant colony (ACO) optimisation algorithm optimised BPNN algorithm and cuckoo search (CS) optimised BPNN algorithm have been implemented to predict wheat aphid, which was validated with the ground truth information measured by hand-held instruments on the ground. The prediction results of each algorithm have been analysed. The major original contributions of this thesis are as follows: 1. A variety of optimisation algorithms are used to improve the regression analysis of the BPNN algorithm, so that the prediction results of each model for wheat growth, yield and aphid are more accurate. 2. The spectral characteristics of winter wheat canopy have been analysed. The correlation between the absorption band and the associated physical and chemical properties of crops, specially the red edge slope, with the crop yield and wheat aphid damage is established. 3. Adjusted MSE and un-centered R-square, as accurate evaluation criteria for practical applications, are used to compare the prediction results of the models under different dimensions of the observed data. 4. Improve algorithm training by using the cross-validation method to obtain reliable and stable models for the prediction of wheat growth, yield, and aphid. Through repeated cross-validation, a better model can be obtained in the last. Key word：Precision agriculture; BP network, wheat growth assessment; wheat yield prediction, wheat aphid validatio

Dark Green Color Index as a Method of Real-time In-season Corn Nitrogen Measurement and Fertilization

Corn (Zea mays L.) requires higher rates of nitrogen fertilizer than any other major U.S. crop partly because N fertilizers are subject to loss through various mechanisms. Because of this, corn may suffer from inadequate nitrogen fertilization or producers may over-apply nitrogen to compensate for early-season nitrogen losses. A timely, accurate, and precise method for measuring in-season corn N status is needed to allow producers to keep nitrogen use efficiency high within a growing season. Using appropriate software, hue, saturation, and brightness values of digital images can be combined in a dark green color index (DGCI) which is closely associated with leaf nitrogen concentration. Our objectives were: (1) to develop quantitative relationships among yield, corn leaf nitrogen concentration, and DGCI measurements taken in the mid-vegetative stages of growth development;; and (2) to determine the amount of nitrogen to apply to recover yield based upon DGCI measurements on 6-leaf corn (V6). Various corn hybrids were planted across two years in Arkansas. A wide range of N applications were made at emergence and at V6 stage. SPAD, DGCI, and leaf nitrogen measurements were taken prior to V6 application and again at tasseling. Leaf nitrogen concentrations, DGCI, and SPAD were found to be closely associated. Crops with varying early-season N deficiencies demonstrated a non-linear, quadratic response to V6 N applications. Combining the responses of yield to V6 N application amounts with concurrent mid-season DGCI measurements allowed for the development of calibration equations. These calibrations equations allow corrective, mid-season N applications to be made based on an observed DGCI value, which allows for the recovery of 90 or 95% of the crop\u27s yield potential

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

An Integrated Approach for Predicting Nitrogen Status in Early Cotton and Corn

Cotton (Gossypium hirsutum L.) and corn (Zea mays L.) spectral reflectance holds promise for deriving variable rate N (VRN) treatments calibrated with red-edge inflection (REI) type vegetation indices (VIs). The objectives of this study were to define the relationships between two commercially available sensors and the suitable VIs used to predict N status. Field trials were conducted during the 2012-2013 growing seasons using fixed and variable N rates in cotton ranging from 33.6-134.4 kg N ha-1 and fixed N rates in corn ranging from 0.0 to 268.8 kg N ha-1. Leaf N concentration, SPAD chlorophyll and crop yield were analyzed for their relation to fertilizer N treatment. Sensor effects were significant and red-edge VIs most strongly correlated to N status. A theoretical ENDVI index was derived from the research dataset as an improvement and alternative to the Guyot’s Red Edge Inflection and Simplified Canopy Chlorophyll Content Index (SI)

A Cost-effective Multispectral Sensor System for Leaf-Level Physiological Traits

With the concern of the global population to reach 9 billion by 2050, ensuring global food security is a prime challenge for the research community. One potential way to tackle this challenge is sustainable intensification; making plant phenotyping a high throughput may go a long way in this respect. Among several other plant phenotyping schemes, leaf-level plant phenotyping needs to be implemented on a large scale using existing technologies. Leaf-level chemical traits, especially macronutrients and water content are important indicators to determine crop’s health. Leaf nitrogen (N) level, is one of the critical macronutrients that carries a lot of worthwhile nutrient information for classifying the plant’s health. Hence, the non-invasive leaf’s N measurement is an innovative technique for monitoring the plant’s health. Several techniques have tried to establish a correlation between the leaf’s chlorophyll content and the N level. However, a recent study showed that the correlation between chlorophyll content and leaf’s N level is profoundly affected by environmental factors. Moreover, it is also mentioned that when the N fertilization is high, chlorophyll becomes saturated. As a result, determining the high levels of N in plants becomes difficult. Moreover, plants need an optimum level of phosphorus (P) for their healthy growth. However, the existing leaf-level P status monitoring methods are expensive, limiting their deployment for the farmers of low resourceful countries. The aim of this thesis is to develop a low-cost, portable, lightweight, multifunctional, and quick-read multispectral sensor system to sense N, P, and water in leaves non-invasively. The proposed system has been developed based on two reflectance-based multispectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). By deploying state of the art machine learning algorithms, the spectroscopic information is modeled and validated to predict that nutrient status. A total of five experiments were conducted including four on the greenhouse-controlled environment and one in the field. Within these five, three experiments were dedicated for N sensing, one for water estimation, and one for P status determination. In the first experiment, spectral data were collected from 87 leaves of canola plants, subjected to varying levels of N fertilization. The second experiment was performed on 1008 leaves from 42 canola cultivars, which were subjected to low and high N levels, used in the field experiment. The K-Nearest Neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the first experiment and 79.2% for the second experiment. In the third and fourth experiments, spectral data were collected from 121 leaves for N and 186 for water experiments respectively; and Rational Quadratic Gaussian Process Regression (GPR) algorithm is applied to correlate the reflectance data with actual N and water content. By performing 5-fold cross-validation, the N estimation shows a coefficient of determination (R^2) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola shows an R^2 of 18.02%, corn of 68.41%, soybean of 46.38%, and wheat of 64.58%. Finally, the fifth experiment was conducted on 267 leaf samples subjected to four levels of P treatments, and KNN exhibits the best accuracy, on the test set, of about 71.2%, 73.5%, and 67.7% for corn, soybean, and wheat, respectively. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf N and P status/content. However, further investigation is needed to improve the water estimation results using the proposed device. Moreover, the utility of the device to estimate other nutrients as well as other crops has great potential for future research