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

Agricultural Research Service research highlights in remote sensing for calendar year 1980

The AR research mission in remote sensing is to develop the basic understanding of the soil plant animal atmosphere continuum in agricultural ecosystems and to determine when remotely sensed data can be used to provide information about these agricultural ecosystems. A brief statement of the significant results of each project is given. A list of 1980 publication and location contacts is also given

Potential for Optical Sensor-Based Nitrogen Fertilization in Grain Sorghum (Sorghum bicolor L. Moench) in Arkansas

Ground-based active-optical (GBAO) crop sensors have become an effective tool to improve nitrogen (N) use efficiency and to predict yield early in the growing season, particularly for grass crops. Commercially available canopy sensors calculate the normalized difference vegetative index (NDVI) by emitting light in the red and near infrared range of the electromagnetic spectrum. The NDVI is used to evaluate vigor status and to estimate yield potential. However, few studies have been conducted to compare the performance of commercially available sensors. Therefore, a study was conducted using the most common crop canopy sensors: i) N-Tech\u27s GreenSeekerTM (GS), ii) Holland Scientific\u27s Crop CircleTM (CC), and iii) Minolta\u27s SPAD-502 chlorophyll content meter (CCM). The objective of this study was to find the optimum time for sensing and compare the relative performance of the sensors in estimating the yield potential of grain sorghum (Sorghum bicolor L. Moench). Treatments included six levels of N fertilization (0, 37, 74, 111, 148, and 185 kg N/ ha), applied in a single split 20 days after planting (DAP). Treatments were arranged in a randomized complete block design with five replications, in four locations in Arkansas, during 2012 and 2013. Sensors readings at vegetative growth stages V3, 4, 5 and 6. Results from simple regression analysis showed that the V3-V4 growth stage correlated better with grain yield than readings collected and any other time. In season estimated yield (INSEY) obtained at V3 captured 41, 57, 78, and 61% of the variation in grain sorghum yield when red NDVI of GS, red NDVI of CC, red edge for CC and CCM, respectively, were used. Results from these studies suggest that the CC sensor has a better potential for in-season site-specific N application in Arkansas than the GS sensor. The GS reflectance values appear to saturate after the V3 stage, in contrast with CC values that allow for discrimination past the V3 Stage. Therefore, the red edge wavebands of CC appear to be better suited to develop relationships between spectral vegetation indices and agronomic parameters

What Stages In The Phenology Of Corn Are The Most Correlated With Rainfed Corn Yields In The Corn Belt Using Remote Sensing?

Two weekly and freely available remotely sensed vegetation indices, Vegetation Condition Index (VCI) and Temperature Condition Index (TCI), were assessed for state level corn yield correlation in the Corn Belt region of the United States for the years of 2007-2013. VCI and TCI were 16-km pixels which are derived from the Advanced Very High Resolution Radiometer (AVHRR). Corn pixels were identified by downloading yearly USDA Cropscape pixels for corn in each state. Irrigated corn pixels were removed by using the 2007 Irrigated Agriculture Dataset developed by Pervez and Brown (2010) as a mask. Corn pixels were then resampled to 16-km in ArcGIS 10.1, with only pixels with greater than 50% corn coverage being drawn. These corn pixels were then used to identify VCI and TCI corn pixels for each state. Weekly VCI and TCI corn pixel values were then averaged for each state and correlated with yield from the National Agriculture Statistics Service (NASS). For the Corn Belt as a whole, VCI had high positive correlation in Week 34 and TCI has high positive correlation in Week 28. The highest correlating VCI and TCI weeks for each state were then used for regression with yield. Seven of the 12 states had R2 values greater than 0.7, meaning at least 70 percent of the variation in yield for seven of the states can be explained by VCI and TCI

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI \u3c 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes

Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1 ) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops

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