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Use Of Small Unmanned Aerial System For Validation Of Sudden Death Syndrome In Soybean Through Multispectral And Thermal Remote Sensing
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Book Synopsis Use of Small Unmanned Aerial System for Validation of Sudden Death Syndrome in Soybean Through Multispectral and Thermal Remote Sensing by : Nicholle M. Hatton
Download or read book Use of Small Unmanned Aerial System for Validation of Sudden Death Syndrome in Soybean Through Multispectral and Thermal Remote Sensing written by Nicholle M. Hatton and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Assessment of Sudden Death Syndrome by Utilizing Unmanned Aerial Vehicles and Multispectral Imagery by : Lindsey McKinzie
Download or read book Assessment of Sudden Death Syndrome by Utilizing Unmanned Aerial Vehicles and Multispectral Imagery written by Lindsey McKinzie and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fusarium virguliforme is a soil-borne pathogen that is the causal agent of sudden death syndrome (SDS). This disease is one of the top contributors to major yield losses in soybean across the United States. Characteristic symptoms of the disease include interveinal chlorosis and/or necrosis of trifoliate leaves and defoliation. In some cases, the foliar symptoms may not be present, but yield loss still occurs. This disease is evaluated using an incidence rating, the percent of plants in the plot that are expressing symptoms, and a severity rating, using a one to nine scale based on varying levels of chlorosis, necrosis, and defoliation. Using remote sensing provides an alternate approach to identify and evaluate plant diseases. It provides a non-destructive method to assess the severity of foliar symptoms and their distribution across production fields. SDS was chosen as the disease to use for this system due to the unique disease symptomology and yield loss. In 2019 and 2020, SDS trials were established in a production field location that has a history of SDS in Valmeyer, IL. This seed treatment study had different chemicals with varying levels of efficacy against SDS. Disease ratings were collected at the first sign of symptoms, and aerial imagery was collected on the same day. There were multiple dates across both years when this data was collected. ArcGIS was used to analyze the multispectral imagery and do a plot by plot analysis for each of the plots. A regression analysis was performed to test the relationship between the foliar disease ratings and the plot data collected from the multispectral imagery. Multiple vegetation indices were tested, and the results showed that overall, in 2019, GNDVI had the strongest relationship with foliar ratings. In 2020, NDRE had the strongest overall relationship with foliar ratings. The relationship between NDVI and the ratings was the most consistent at the last rating of the season.
Book Synopsis Innovative Payloads for Small Unmanned Aerial System-Based Personal Remote Sensing and Applications by : Austin M. Jensen
Download or read book Innovative Payloads for Small Unmanned Aerial System-Based Personal Remote Sensing and Applications written by Austin M. Jensen and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing enables the acquisition of large amounts of data, over a small period of time, in support of many ecological applications (i.e. precision agriculture, vegetation mapping, etc.) commonly from satellite or manned aircraft platforms. This dissertation focuses on using small unmanned aerial systems (UAS) as a remote sensing platform to collect aerial imagery from commercial-grade cameras and as a radio localization platform to track radio-tagged sh. The small, low-cost nature of small UAS enables remotely sensed data to be captured at a lower cost, higher spatial and temporal resolution, and in a timelier manner than conventional platforms. However, these same attributes limit the types of cameras and sensors that can be used on small UAS and introduce challenges in calibrating the imagery and converting it into actionable information for end users. A major contribution of this dissertation addresses this issue and includes a complete description on how to calibrate imagery from commercial-grade visual, near-infrared, and thermal cameras. This includes the presentation of novel surface temperature sampling methods, which can be used during the flight, to help calibrate thermal imagery. Landsat imagery is used to help evaluate these methods for accuracy; one of the methods performs very well and is logistically feasible for regular use. Another major contribution of this dissertation includes novel, simple methods to estimate the location of radio-tagged sh using multiple unmanned aircraft (UA). A simulation is created to test these methods, and Monte Carlo analysis is used to predict their performance in real-world scenarios. This analysis shows that the methods are able to locate the radio-tagged sh with good accuracy. When multiple UAs are used, the accuracy does not improve; however the sh is located much quicker than when one UA is used.
Book Synopsis Using Multispectral Drone Imaging and Machine Learning to Monitor Soybean Cyst Nematodes by : Joseph Moses Kalinzi
Download or read book Using Multispectral Drone Imaging and Machine Learning to Monitor Soybean Cyst Nematodes written by Joseph Moses Kalinzi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soybean Cyst Nematode (SCN) poses a significant threat to soybean production in North America and the world at large. Early and accurate detection of SCN infestations is crucial for implementing effective management strategies and minimizing yield losses. The conventional method of SCN detection involves uprooting plants to examine the roots and collecting soil samples. Drone-based multispectral imaging has been used as a viable alternative for crop monitoring due to its detailed spatial and spectral information and scheduling flexibility. This thesis aims to examine the potential of using multispectral drone images for SCN detection in a soybean production field and develop a non-destructive approach to support improved precision agricultural management practices. Using the DJI Matrice 210 drone and a MicaSense Altum sensor, at a height of 50 meters above ground level and top speed of 6 meters/second, a total of 2,550 multispectral images per flight were collected for a total of fourteen flights beginning in June 2022 up to September 2022 from a production field with variable SCN infestation levels located in Carmi, IL. These images were postprocessed with geometric and radiometric correction to produce orthomosaic photos. Ten vegetation indices namely, NDRE, NDVI, EVI, GNDVI, BNDVI, SIPI, R-EDGE/G, NIR/G, R-EDGE/R and MSR, were computed for each flight date and study plot. The count of SCN eggs was appended to each study plot to find the correlation between the vegetation indices and the field parameters. The VIs having the highest correlation with the eggs and also having the highest number of correlation coefficients significantly different from zero were NDRE, NDVI and GNDVI. I computed the mean values of these VIs for each study plot and flight date which resulted into a time-series trend analysis. To identify study plots with similar trends, an agglomerative hierarchical clustering was performed which resulted into two clusters for each VI. After conducting the ANOVA test, NDVI returned statistically significant results for all the field parameters, GNDVI returned one while NDRE returned three outcomes that were not statistically significant. The study plots belonging to Cluster 1 had a higher mean of SCN count while those in Cluster 2 portrayed little or no SCN. I found NDVI to be the optimal VI because the results from statistical tests and modeling techniques conducted were significant for all SCN parameters, such as cyst and egg count for the plots clustered based on the NDVI trend. Therefore, I used the plots clustered based on the NDVI trend to train and test six ML classification models (Support Vector Classifier, Naïve Bayes, K-Nearest Neighbors, Linear Discriminant Analysis, MLP-Neural Network and Gradient Boost) such that when presented with information in a format like that used in training, it becomes possible to identify plots with high or no SCN. Gradient Boost, MLP-NN and LDA performed with 89%, 82% and 80% accuracy respectively.
Book Synopsis Using Remote Sensing in Soybean Breeding by : Hatice Aslan
Download or read book Using Remote Sensing in Soybean Breeding written by Hatice Aslan and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing technologies might serve as indirect selection tools to improve phenotyping to differentiate genotypes for yield in soybean breeding program as well as the assessment of soybean cyst nematode (SCN), Heterodera glycines. The objective of these studies were to: i) investigate potential use of spectral reflectance indices (SRIs) and canopy temperature (CT) as screening tools for soybean grain yield in an elite, segregating population; ii) determine the most appropriate growth stage(s) to measure SRI's for predicting grain yield; and iii) estimate SCN population density among and within soybean cultivars utilizing canopy spectral reflectance and canopy temperature. Experiment 1 was conducted at four environments (three irrigated and one rain-fed) in Manhattan, KS in 2012 and 2013. Each environment evaluated 48 F4- derived lines. In experiment 2, two SCN resistant cultivars and two susceptible cultivars were grown in three SCN infested field in Northeast KS, in 2012 and 2013. Initial (Pi) and final SCN soil population (Pf) densities were obtained. Analyses of covariance (ANCOVA) revealed that the green normalized vegetation index (GNDVI) was the best predictive index for yield compared to other SRI's and differentiated genotype performance across a range of reproductive growth stages. CT did not differentiate genotypes across environments. In experiment 2, relationships between GNDVI, reflectance at single wavelengths (675 and 810 nm) and CT with Pf were not consistent across cultivars or environments. Sudden death syndrome (SDS) may have confounded the relationships between remote sensing data and Pf. Therefore, it would be difficult to assess SCN populations using remote sensing based on these results.
Book Synopsis Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing by :
Download or read book Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing written by and published by . This book was released on 1981 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Soybean Sudden Death Syndrome by : X. B. Yang
Download or read book Soybean Sudden Death Syndrome written by X. B. Yang and published by . This book was released on 1998 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis SUAS Sensor Selection and Utilization for Remotely Sensing Crop Health and Production Parameters by : Harman Singh Sangha
Download or read book SUAS Sensor Selection and Utilization for Remotely Sensing Crop Health and Production Parameters written by Harman Singh Sangha and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the beginning of 21st century technological innovations are aiding to improve the agricultural industry. Precision Agriculture (PA) systems are one of those technologies which are creating the greatest impact on agricultural practices. PA is defined as the farming practices which optimizes the farm inputs with a goal to increase overall yield and minimizing harmful environmental impacts. To accomplish such targets, collection of high resolution spatial and temporal data on crop health and physiology becomes critical in helping to make decisions on optimizing farm inputs. Due to this, there has been increase in the use of small unmanned aerial systems (sUAS) in agricultural industry as a PA tool. sUAS are used for remote sensing applications such as crop phenotyping, crop water stress estimation, crop disease detection among others. With the development of robust sUAS sensors, collection of vegetative based data in field is evolving into standalone system. Sensors chosen for such applications include RGB sensors, thermal infrared sensors, modified color infrared sensors, multispectral sensors and Hyperspectral sensors. Usually a sUAS sensor is selected based on the spectral ability of the sensor and specific configuration are not considered. Improper selection of sensors leads to false data collection affecting the studies done using these sensors. In this thesis thermal infrared, modified color infrared and multispectral sensors were identified and compared based on the parameters specific to the sensor involved, to understand and improve future selection of sUAS sensors. Multispectral sensors are more commonly used as compared to other UAS sensors as multispectral sensors provide information both in visible and infrared spectrum. Multispectral sensors can be divided into two categories, namely narrowband and broadband. A study was done to compare and evaluate performance of the two types of multispectral sensors available in precision agriculture systems. The sensors were examined on different parameters to check their ability to provide remote sensing data with high-accuracy. Spectral response, ground resolution and statistical correlation with ground data were evaluated for both the sensors. Results shows the sensors performed differently in different parameters, but the spectral data provided by them was in close correlation with each other. A need for developing better ground data collection methods was observed. Thermal image quality is critical to accurately quantify water stress patterns in field crops. Image data quality from a thermal sensor is impacted by several factor. A second experiment was conducted with goal to compare accuracy of canopy temperature quantification and assess the quality of thermal orthomosaic when using thermal sensor of different focal length and image acquisition at varying flying altitudes of a sUAS. Three thermal infrared cameras were selected with focal lengths of 9mm, 13mm, and 19mm. All three cameras were flown at altitudes of 20m, 50m, and 80m. Results showed that 13 mm focal length and 50 m altitude produce finer resolution orthomosaic which provide a robust and accurate information on canopy temperature. Overall, the canopy temperatures were quantified accurately regardless of altitude and focal length by efficiently and accurately utilizing the ground reference system.
Book Synopsis Soybean Sudden Death Syndrome by : Victor Njongu Njiti
Download or read book Soybean Sudden Death Syndrome written by Victor Njongu Njiti and published by . This book was released on 1994 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Utilizing Unmanned Aerial Systems to Sample Insects in Soybean by : Marvin Merkl
Download or read book Utilizing Unmanned Aerial Systems to Sample Insects in Soybean written by Marvin Merkl and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: To overcome some limitations of manual insect sampling in soybeans, an unmanned aerial vehicle (UAV) sampling platform was developed that could collect insects in a sweep net attached to the bottom of a UAV. Before this UAV sampling platform can be used to make management decisions, correlations with manual sweep net and/or drop cloth sampling methods are needed. This will allow action thresholds for the various pests to be calculated for the UAV sampling platform. To make the correlations, 87 soybean fields were sampled during 2020 and 2021 with each of 4 sampling methods, a UAV travelling 50-m, the same UAV travelling 25-m, 25 manual sweeps with sweep net, and a 1.5-row-m sample on a drop cloth. Data were compiled for 12 insect pests of soybeans in 5 families. Significant positive correlations between all sampling methods showed that all methods were useful for sampling all the insects of interest.
Download or read book SOYBEAN SUDDEN DEATH SYNDROME written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Use of Remote Multispectral Sensing in Agriculture by : William Lawerence Parks
Download or read book The Use of Remote Multispectral Sensing in Agriculture written by William Lawerence Parks and published by . This book was released on 1973 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Evaluating Usefulness of 10 Genomic Regions Associated with Sudden Death Syndrome Resistance in Soybean by : Alexander Stephen Luckew
Download or read book Evaluating Usefulness of 10 Genomic Regions Associated with Sudden Death Syndrome Resistance in Soybean written by Alexander Stephen Luckew and published by . This book was released on 2012 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Morphological and Anatomical Effects of Sudden Death Syndrome on Soybean Leaflets by : John Oliver
Download or read book Morphological and Anatomical Effects of Sudden Death Syndrome on Soybean Leaflets written by John Oliver and published by . This book was released on 1988 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the Potential of Small UAS for Multispectral Remote Sensing in Large-scale Agricultural and Archaeological Applications by : Rebecca Retzlaff
Download or read book On the Potential of Small UAS for Multispectral Remote Sensing in Large-scale Agricultural and Archaeological Applications written by Rebecca Retzlaff and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimal Remote Sensing with Small Unmanned Aircraft Systems and Risk Management by :
Download or read book Optimal Remote Sensing with Small Unmanned Aircraft Systems and Risk Management written by and published by . This book was released on 2017 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, the rapid rise of Unmanned Aircraft Systems (UASs) has blossomed into a new component of the aviation industry. Though regulations within the United States lagged, the promise of the ability of Small Unmanned Aircraft Systems (SUASs), or those UAS that weigh less than 55 lbs, has driven significant advances in small scale aviation technology. The dream of a small, low-cost aerial platform that can fly anywhere and keep humans safely away from the 'dull, dangerous and dirty' jobs, has encouraged many to examine the possibilities of utilizing SUAS in new and transformative ways, especially as a new tool in remote sensing. However, as with any new tool, there remains significant challenges in realizing the full potential of SUAS-based remote sensing. Within this dissertation, two specific challenges are addressed: validating the use of SUAS as a remote sensing platform and improving the safety and management of SUAS. The use of SUAS in remote sensing is a relatively new challenge and while it has many similarities to other remote sensing platforms, the dynamic nature of its operation makes it unique. In this dissertation, a closer look at the methodology of using SUAS reveals that while many view SUAS as an alternative to satellite imagery, this is an incomplete view and that the current common implementation introduces a new source of error that has significant implications on the reliability of the data collected. It can also be seen that a new approach to remote sensing with an SUAS can be developed by addressing the spatial, spectral and temporal factors that can now be more finely adjusted with the use of SUAS. However, to take the full advantage of the potential of SUAS, they must uphold the promise of improved safety. This is not a trivial challenge, especially for the integration into the National Airspace System (NAS) and for the safety management and oversight of diverse UAS operations. In this dissertation, the challenge of integrating SUAS in the NAS is addressed by presenting an analysis of enabling flight operations at night, developing a swarm safety management system for improving SUAS robustness, investigating the use of new technology on SUAS to improve air safety, and developing a novel framework to better understand human-SUAS interaction. Addressing the other side of safety, this dissertation discusses the struggle of large diverse organizations to balance acceptance, safety and oversight for UAS operations and the development of a novel implementation of a UAS Safety Management System.
Book Synopsis Toward Improved Crop Management Using Spectral Sensing with Unmanned Aerial Systems by : Robert Ormal Chancia
Download or read book Toward Improved Crop Management Using Spectral Sensing with Unmanned Aerial Systems written by Robert Ormal Chancia and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Remote sensing applications in agriculture are established for large-scale monitoring with satellite and airborne imagery, but unmanned aerial systems (UAS) are poised to bring in-field mapping capabilities to the hands of individual farmers. UAS imaging holds several advantages over traditional methods, including centimeter-scale resolution, reduced atmospheric absorption, flexible timing of data acquisitions, and ease of use. In this work, we present two studies using UAS imaging of specialty crops in upstate New York to work towards improved crop management applications. The first study is an investigation of multispectral imagery obtained over table beet fields in Batavia, NY during the 2018 and 2019 seasons to be used in root yield modeling. We determined optimal growth stages for future observations and establish the importance of quantifying early growth via determination of canopy area, a feature unattainable with lower resolution imaging. We developed models for root mass and count based on area-augmented imagery of our raw study plots and their corresponding ground truth data for practical testing with independent data sets. The second study was designed to determine an optimal subset of wavelengths derived from hyperspectral imagery that are related to grapevine nutrients for improved vineyard nutrient monitoring. Our ensemble wavelength selection and regression algorithm chose wavelengths consistent with known absorption features related to nitrogen content in vegetation. Our model achieved a leave-one-out cross-validation root-mean-squared error of 0.17% nitrogen in our dried vine-leaf samples with 2.4-3.6% nitrogen. This is an improvement upon published studies of typical UAS multispectral sensors used to assess grapevine nitrogen status. With further testing on new data, we can determine consistently selected wavelengths and guide the design of specialty multispectral sensors for improved grapevine nutrient management."--Abstract.
