Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales

Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing

On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity

Implementing an Agro-Environmental Information System (AEIS) Based on GIS, Remote Sensing, and Modelling -- A case study for rice in the Sanjiang Plain, NE-China

Information on agro-ecosystems is crucial for understanding the agricultural production and its impacts on the environment, especially over large agricultural areas. The Sanjiang Plain (SJP), covering an area of 108 829 km², is a critical food base located in NE-China. Rice, soya bean and maize are the major crops in the SJP which are sold as commercial grain throughout China. The aim of this study is to set up an Agro-Environmental Information System (AEIS) for the SJP by employing the technologies of geographic information systems (GIS), remote sensing (RS), and agro-ecosystem modelling. As the starting step, data carrying interdisciplinary information from multiple sources are organized and processed. For an AEIS, geospatial data have to be acquired, organized, operated, and even regenerated with good positioning conditions. Georeferencing of the multi-source data is mandatory. In this thesis, high spatial accuracy TerraSAR-X imagery was used as a reference for georeferencing raster satellite data and vector GIS topographic data. For the second step, the georeferenced multi-source data with high spatial accuracy were integrated and categorized using a knowledge-based classifier. Rice was analysed as an example crop. A rice area map was delineated based on a time series of three high resolution FORMOSAT-2 (FS-2) images and field observed GIS topographic data. Information on rice characteristics (i.e., biomass, leaf area index, plant nitrogen concentration and plant nitrogen uptake) was derived from the multi-temporal FS-2 images. Spatial variability of rice growing status on a within-field level was well detected. As the core part of the AEIS, an agro-ecosystem modelling was then applied and subsequently crops and the environmental factors (e.g., climate, soil, field management) are linked together through a series of biochemical functions inherent in the modelling. Consequently, the interactions between agriculture and the environment are better interpreted. In the AEIS for the SJP, the site-specific mode of the DeNitrification-DeComposition (DNDC) model was adapted on regional scales by a technical improvement for the source code. By running for each pixel of the model input raster files, the regional model assimilates raster data as model inputs automatically. In this study, detailed soil data, as well as the accurate field management data in terms of crop cultivation area (i.e. rice) were used as model inputs to drive the regional model. Based on the scenario optimized from field observation, rice yields over the Qixing Farm were estimated and the spatial variability was well detected. For comparison, rice yields were derived from multi-temporal FS-2 images and the spatial patterns were analysed. As representative environmental effects, greenhouse gas of nitrous oxide (N2O) and carbon dioxide (CO2) emitted from the paddy rice fields were estimated by the regional model. This research demonstrated that the AEIS is effective in providing information about (i) agriculture on the region, (ii) the impacts of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies, especially for the regional areas (e.g. the SJP) that is lacking of geospatial data

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

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

Land Use/Land Cover Change and Its Hydrological Impacts from 1984 to 2010 in the Little River Watershed, Tennessee

Land use/land cover (LULC) change, especially the conversion from farmland to residential and commercial land, has led to significant environmental issues in changing fluvial dynamics, accelerating sediment erosion and degrading water quality. The Little River, which provides drinking water for over 100,000 residents in Blount County, Tennessee, and serves as a source of agriculture and recreational activities, was listed as one of the U.S. Environmental Protection Agency’s (EPA) Targeted Watersheds because the water quality of its tributaries has become impaired due to several reasons. In this study, a detailed record of LULC change in a roughly 2-year interval was documented from 1984 to 2010 based on the classification of Landsat TM/ETM+ images. The classification accuracy was assessed by the comparison of Google Earth high resolution images in 2010. Then, the Soil and Water Assessment Tool (SWAT), a physically-based distributed hydrological model, was used to quantify the impacts of LULC change on streamflow and water quality in this watershed over this period. The results showed that Landsat TM/ETM+ images can be classified accurately using the Maximum Likelihood Classification (MLC) algorithm, and the SWAT model can effectively simulate the long-term impact of LULC change on streamflow and non-point source (NPS) pollution in this watershed. Above 80% overall accuracy and the kappa coefficient were achieved in the accuracy assessment of the classification of year 2010. Long-term classified LULC records indicated that urban areas (residential and commercial lands) and forest increased in 1984-2010 from 6.3 to 11.1% and from 65.0 to 69.5%, respectively, whereas agricultural land decreased from 28.3 to 18.9% over the same period. After calibration and validation, the simulation results indicated that stream flow increased 3% in this whole watershed, but with a very distinct spatial pattern. The model also suggested sediment load and nutrients (total nitrogen and phosphorus) had different degrees of decline. The statistic analysis showed that the increase of streamflow and urban expansion demonstrated a very strong and positive relationship, and water quality change is highly related to the decrease of agricultural land that occurred in this watershed in recent years

Estimating Soil Organic Carbon in Cultivated Soils Using Soil Test Data, Remote Sensing Imagery from Satellites (Landsat 8 and PlantScope), and Web Soil Survey Data

Soil organic carbon (SOC) is an important soil parameter of cultivated soils that needs to be monitored and mapped regularly to enhance soil health and productivity. SOC levels in cultivated areas is difficult to monitor for farmers and is costly to analyze using traditional methods. The objective of this study was to estimate surface SOC distribution in selected soils of Major Land Resource Areas (MLRA) 102A (Rolling Till Plain, Brookings County, SD) and 103 (Central Iowa and Minnesota Till Prairies, Lac qui Parle County, MN) using soil sample data, Web Soil Survey (WSS) data, and satellite imagery (Landsat 8 and PlanetScope). Different satellite imagery bands and band combinations were used to reach more accurate results. The dominant soils in the area are Haplustolls, Calciustolls, and Endoaquolls formed in silty sediments, local silty alluvium, and till. Sites were selected and soil samples were collected in May 2018 after planting. SOC and soil properties were measured at the 0-15 cm depth. SOC was mainly affected by soil texture in the studied selected soils. Multiple-linear regression was used to build SOC prediction models from soil test data. The final SOC model (using stepwise regression) is SOCp = 3.98 + (-0.210 pH) + (-0.220 Sand [g kg-1]) + (0.040 Sum of Extractable Cation, SOEC [cmolc kg-1]). The Ridge Regression (RR) (CV = 0.066, MSE = 0.063) and Principal Component Regression (PCR) (CV = 0.071, MSE = 0.068) were used to deal with multicollinearity and RR was determined to be as the best model, with 82.7% of variation in SOC explained by the RR model. Landsat 8 and PlanetScope spectral bands and different indices were also used to develop SOC prediction models. The stepwise regression analyses revealed that the Landsat 8 prediction model had multicollinearity problem. Ridge regression and PCR were applied, and RR was chosen as the best model with SOCp = -26.7 + (0.310 BSIL) + (-23.2 Band 5L) + (75.8 Band 2L) + (-51.1 Band 3L) + ( -3.05 Band 7L). The RR model (CV = 0.24, MSE = 0.22) explained 37.0% of the variation in SOC for Landsat 8. The reduced PlanetScope model was SOCp = -25.1 + (2980 Band4P) + (0.327 BSIP). Approximately 60.0% of the variation in SOC was explanined by the Ordinary Least Square (OLS) (CV = 0.15, MSE = 0.14) model and was free of multicollinearity. WSS data showed similar patterns as soil test data for SOC predictions. The best model for WSS data was a linear regression, SOCp = 3.37 + (-0.0200 Sand WSS [g kg-1]) and 49.0% of the variation in SOC was explained by this model. WSS data were then added as variables into the spatial (satellite) estimation models. The Landsat 8 and WSS data explained 53.3%, PlanetScope and WSS data explained 68.8% of the SOC variation. Based on these results, deciding on the number of soil sampling points, and the use of specific variables in the model is very crucial for the model development. Estimating SOC by minimizing the number of needed soil sampling points, using satellite imagery, and public free sources provides an easy, efficient and cost-effective way to monitor SOC levels and identify the best management systems for producers and natural resource managers. This project produced accurate SOC prediction models using soil test data, satellite imagery and Web Soil Survey data. This SOC estimation model helps farmers, resource managers, and researchers to monitor SOC concentration on the soil surface using remote sensing alone, or with WSS data, or with a minimal amount of soil test data

EXPLORING SPATIAL AND TEMPORAL VARIABILITY OF SOIL AND CROP PROCESSES FOR IRRIGATION MANAGEMENT

Irrigation needs to be applied to soils in relatively humid regions such as western Kentucky to supply water for crop uptake to optimize and stabilize yields. Characterization of soil and crop variability at the field scale is needed to apply site specific management and to optimize water application. The objective of this work is to propose a characterization and modeling of soil and crop processes to improve irrigation management. Through an analysis of spatial and temporal behavior of soil and crop variables the variability in the field was identified. Integrative analysis of soil, crop, proximal and remote sensing data was utilized. A set of direct and indirect measurements that included soil texture, electrical conductivity (EC), soil chemical properties (pH, organic matter, N, P, K, Ca, Mg and Zn), NDVI, topographic variables, were measured in a silty loam soil near Princeton, Kentucky. Maps of measured properties were developed using kriging, and cokriging. Different approaches and two cluster methods (FANNY and CLARA) with selected variables were applied to identify management zones. Optimal scenarios were achieved with dividing the entire field into 2 or 3 areas. Spatial variability in the field is strongly influenced by topography and clay content. Using Root Zone Water Quality Model 2.0 (RZWQM), soil water tension was modeled and predicted at different zones based on the previous delineated zones. Soil water tension was measured at three depths (20, 40 and 60 cm) during different seasons (20016 and 2017) under wheat and corn. Temporal variations in soil water were driven mainly by precipitation but the behavior is different among management zones. The zone with higher clay content tends to dry out faster between rainfall events and reveals higher fluctuations in water tension even at greater depth. The other zones are more stable at the lower depth and share more similarities in their cyclic patterns. The model predictions were satisfactory in the surface layer but the accuracy decreased in deeper layers. A study of clay mineralogy was performed to explore field spatial differences based on the map classification. kaolinite, vermiculite, HIV and smectite are among the identified minerals. The clayey area presents higher quantity of some of the clay minerals. All these results show the ability to identify and characterize the field spatial variability, combining easily obtainable data under realistic farm conditions. This information can be utilized to manage resources more effectively through site specific application