A Ground-based Real-time Remote Sensing System for Diagnosing Nitrogen Status in Cotton Plants PDF Download
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Author: Ruixiu Sui
Publisher:
ISBN:
Category :
Languages : en
Pages : 246
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Book Description
Author: Ruixiu Sui
Publisher:
ISBN:
Category :
Languages : en
Pages : 246
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Cotton production efficiency has the potential to increase through accurate variable rate application of fertilizer. The ability to variably apply fertilizer already exists. However, an accurate real-time system capable of diagnosing fertilizer deficiencies has yet to be implemented. A ground-based remote sensing system with modulated illumination has been developed for diagnosing nitrogen status in cotton plants. Development of the system was in part based on recommendations from previous research conducted at The University of Tennessee. The prototyped system consists of a multi-spectral sensing unit, a data acquisition and processing unit, and a graphical user interface. The multi-spectral sensing unit utilizes a discriminating artificial illumination source to eliminate error associated with the use of sunlight. Solar angle and atmospheric filtering contribute to variability in light intensity. Narrow spectrum ultra bright LEDs (blue, green, red, and infrared) with peak wavelengths of 466, 540, 644, and 880 nm were used. Modulated light at a frequency of 18.9 kHz was focused into a scanning beam and reflected from the plant canopy. Reflected light from the plant canopy was converted to voltage signals representing reflected light intensity at each waveband. A band pass filter was implemented to pass only the signal due to the modulated light source. The data acquisition and processing system was developed for control of the multi-spectral sensing unit and reliable data collection and processing. The prototyped system was tested on DP451 BRR cotton with four different N application rates. Based on a nitrate analysis, three nitrogen status classifications were identified: low, medium, and high. Analysis of spectral data collected revealed reflected light energy in the red region produced the highest linear correlation with N status (r = -0.7285). A feed forward neural network was trained to predict nitrogen status based on the four spectral measurements taken with the prototyped system. System performance was evaluated based on its ability to correctly classify N status. Results indicate that the system is capable of diagnosing nitrogen status in cotton with a high degree of accuracy. Using dynamically ("1 mi/hr) acquired data, prediction accuracies as high as 91% were achieved when 50% of data was used for training and 50% used for testing. Accuracy decreased slightly to 90% when 25% of the data was used for training and 75% used for testing. Utilizing neural networks, the prototyped system out performed the conventional NDVI andother linear techniques. The system has shown great potential in diagnosing N status in cotton under controlled field conditions. Future testing should be performed to evaluate the system for multiple varieties, growing seasons, and other variables known to contribute to plant health variability.
Author: Marisol Benitez Ramirez
Publisher:
ISBN:
Category :
Languages : en
Pages : 140
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Book Description
Managing nitrogen (N) fertilizer is fundamental to efficient cotton production. Traditional N management strategies often utilize N inefficiently through sub-optimal rate prescriptions and inappropriately timed applications. This leads to reduced production efficiency and increased environmental risk. Both deficiency and excess of N in cotton crop negatively affects lint yield and fiber quality. Thus, the aim is to monitor in-season cotton N levels in realtime at a growth stage where supplemental N can be applied. Research has shown high correlation of cotton leaf N concentrations with spectral reflectance of plants. The GreenSeeker® sensor is a ground-based active-light sensor developed to nondestructively evaluate N status in crops. However, the Normalized Difference Vegetation Index (NDVI) reported by the sensor is subject to influence by the soil background. The objective of this research was to develop an algorithm that improves a ground-based sensing system's ability to discriminate between plant biomass and soil, allowing it to better estimate N status in cotton. Three cotton varieties, three seeding rates, and four N rates were established in a field experiment in Milan, TN. GreenSeeker readings and ultrasonic plant height data were collected and analyzed to investigate the influence of these crop management factors on NDVI. Strong positive correlation (r>0.72) between NDVI and plant height was confirmed. Seeding rate affected NDVI throughout the season, confirming an effect of soil background noise on NDVI values. To aid in algorithm creation, NDVI data were collected from a subset of plots, the plant population was thinned, and re-sensed. Difference in NDVI of these populations was minimized when data below a threshold was removed prior to index calculation. Two algorithms were identified that reduced vegetation indices difference to within the published error of the sensor. The reduction of plant population effect on NDVI was validated by post-processing a larger data set using both algorithms.
Author: Theodore Simos
Publisher: CRC Press
ISBN: 9004155929
Category : Mathematics
Languages : en
Pages : 586
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Book Description
Lecture Series on Computer and on Computational Sciences (LSCCS) aims to provide a medium for the publication of new results and developments of high-level research and education in the field of computer and computational science. In this series, only selected proceedings of conferences in all areas of computer science and computational sciences will be published. All publications are aimed at top researchers in the field and all papers in the proceedings volumes will be strictly peer reviewed. The series aims to cover the following areas of computer and computational sciences: Computer Science Hardware Computer Systems Organization Software Data Theory of Computation Mathematics of Computing Information Systems Computing Methodologies Computer Applications Computing Milieu Computational Sciences Computational Mathematics, Theoretical and Computational Physics, Theoretical and Computational Chemistry Scientific Computation Numerical and Computational Algorithms, Modeling and Simulation of Complex System, Web-Based Simulation and Computing, Grid-Based Simulation and Computing Fuzzy Logic, Hybrid Computational Methods, Data Mining and Information Retrieval and Virtual Reality, Reliable Computing, Image Processing, Computational Science and Education
Author: Huihui Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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In this study, the performances of the optical sensors and instruments carried on both ground-based and airborne platforms were evaluated for monitoring crop growing status, detecting the vegetation response to aerial applied herbicides, and identifying crop nitrogen status. Geostatistical analysis on remotely sensed data was conducted to investigate spatial structure of crop canopy normalized difference vegetation index and multispectral imagery. A computerized crop monitoring system was developed that combined sensors and instruments that measured crop structure and spectral data with a global positioning system. The integrated crop monitoring system was able to collect real-time, multi-source, multi-form, and crop related data simultaneously as the tractor-mounted system moved through the field. This study firstly used remotely sensed data to evaluate glyphosate efficacy on weeds applied with conventional and emerging aerial spray nozzles. A weedy field was In this study, the performances of the optical sensors and instruments carried on both ground-based and airborne platforms were evaluated for monitoring crop growing status, detecting the vegetation response to aerial applied herbicides, and identifying crop nitrogen status. Geostatistical analysis on remotely sensed data was conducted to investigate spatial structure of crop canopy normalized difference vegetation index and multispectral imagery. A computerized crop monitoring system was developed that combined sensors and instruments that measured crop structure and spectral data with a global positioning system. The integrated crop monitoring system was able to collect real-time, multi-source, multi-form, and crop related data simultaneously as the tractor-mounted system moved through the field. This study firstly used remotely sensed data to evaluate glyphosate efficacy on weeds applied with conventional and emerging aerial spray nozzles. A weedy field was set up in three blocks and four aerial spray technology treatments were tested. Spectral reflectance measurements were taken using ground-based sensors from all the plots at 1, 8, and 17 days after treatment. The results indicated that the differences among the treatments could be detected with spectral data. This study could provide applicators with guidance equipment configurations that can result in herbicide savings and optimized applications in other crops. The main focus of this research was to apply sensor fusion technology to ground-based and airborne imagery data. Experimental plots cropped with cotton and soybean plants were set up with different nitrogen application rates. The multispectral imagery was acquired by an airborne imaging system over crop field; at the same period, leaf chlorophyll content and spectral reflectance measurements were gathered with chlorophyll meter and spectroradiometer at canopy level on the ground, respectively. Statistical analyses were applied on the data from individual sensor for discrimination with respect to the nitrogen treatment levels. Multisensor data fusion was performed at data level. The results showed that the data fusion of airborne imagery with ground-based data were capable of improving the performance of remote sensing data on detection of crop nitrogen status. The method may be extended to other types of data, and data fusion can be performed at feature or decision level.
Author: Salim Lamine
Publisher: Elsevier
ISBN: 0323914640
Category : Technology & Engineering
Languages : en
Pages : 555
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Book Description
Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones' geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. - Presents a well-integrated collection of chapters, with quality, consistency and continuity - Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts - Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented - Covers geospatial data, with codes available through shared links
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 956
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Author:
Publisher:
ISBN:
Category : Agriculture
Languages : en
Pages : 32
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Author: Abdul Salam
Publisher: Springer Nature
ISBN: 3030508617
Category : Technology & Engineering
Languages : en
Pages : 435
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Book Description
This book provides an in-depth coverage of the most recent developments in the field of wireless underground communications, from both theoretical and practical perspectives. The authors identify technical challenges and discuss recent results related to improvements in wireless underground communications and soil sensing in Internet of Underground Things (IOUT). The book covers both existing network technologies and those currently in development in three major areas of SitS: wireless underground communications, subsurface sensing, and antennas in the soil medium. The authors explore novel applications of Internet of Underground Things in digital agriculture and autonomous irrigation management domains. The book is relevant to wireless researchers, academics, students, and decision agriculture professionals. The contents of the book are arranged in a comprehensive and easily accessible format. Focuses on fundamental issues of wireless underground communication and subsurface sensing; Includes advanced treatment of IOUT custom applications of variable-rate technologies in the field of decision agriculture, and covers protocol design and wireless underground channel modeling; Provides a detailed set of path loss, antenna, and wireless underground channel measurements in various novel Signals in the Soil (SitS) testbed settings.
Author: Luciane Farias de Oliveira
Publisher:
ISBN:
Category : Cotton
Languages : en
Pages : 87
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Book Description
Nitrogen (N) is an essential nutrient for cotton production; consequently there is a tendency to over-apply nitrogen. High N causes excessive vegetative growth and delayed crop maturity, which result in increased cost in pesticides, growth regulator, and defoliant. Currently, the most common methods for obtaining information about the nitrogen needs of cotton are labor-intensive and time-consuming limiting their use. Reflectance sensors offer the potential to diagnose N needs immediately in a spatially intensive manner. The objective of this study was to develop on-the-go sidedress N rate recommendations based on sensor readings and quantify variability during the day for both passive and active sensors mounted above cotton plants. Reflectance was measured with three sensors (Crop Circle, GreenSeeker, and Cropscan) at three growth stages (early square, mid square and early bloom) and at three heights above the cotton canopy (25, 50, and 100 cm) in 2006 and 2007. Results indicated that all three sensors have potential for accurate prediction of optimal N rate. Prediction accuracy was low at the first square stage but acceptable at mid square or early flower. These results suggest that variable-rate N applications to cotton based on real-time reflectance sensor readings are feasible for the mid-square to first flower growth stages. Variability in reflectance values during the day was relatively large for all three sensors. Mid-day was the time with the least error introduced into N rates by drift in sensor readings.
