Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = (1 + e-15.2829x(RAGDDi-0.1944))-1 - (1 + e-11.6517x(RAGDDi-1.0267))-1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status

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

Site-specific seeding using multi-sensor and data fusion techniques : a review

Site-specific seeding (SSS) is a precision agricultural (PA) practice aiming at optimizing seeding rate and depth, depending on the within field variability in soil fertility and yield potential. Unlike other site-specific applications, SSS was not adopted sufficiently by farmers due to some technological and practical challenges that need to be overcome. Success of site-specific application strongly depends on the accuracy of measurement of key parameters in the system, modeling and delineation of management zone maps, accurate recommendations and finally the right choice of variable rate (VR) technologies and their integrations. The current study reviews available principles and technologies for both map-based and senor-based SSS. It covers the background of crop and soil quality indicators (SQI), various soil and crop sensor technologies and recommendation approaches of map-based and sensor-based SSS applications. It also discusses the potential of socio-economic benefits of SSS against uniform seeding. The current review proposes prospective future technology synthesis for implementation of SSS in practice. A multi-sensor data fusion system, integrating proper sensor combinations, is suggested as an essential approach for putting SSS into practice

Spatial combination of sensor data deriving from mobile platforms for precision farming applications

This thesis combines optical sensors on a ground and on an aerial platform for field measurements in wheat, to identify nitrogen (N) levels, estimating biomass (BM) and predicting yield. The Multiplex Research (MP) fluorescence sensor was used for the first time in wheat. The individual objectives were: (i) Evaluation of different available sensors and sensor platforms used in Precision Farming (PF) to quantify the crop nutrition status, (ii) Acquisition of ground and aerial sensor data with two ground spectrometers, an aerial spectrometer and a ground fluorescence sensor, (iii) Development of effective post-processing methods for correction of the sensor data, (iv) Analysis and evaluation of the sensors with regard to the mapping of biomass, yield and nitrogen content in the plant, and (v) Yield simulation as a function of different sensor signals. This thesis contains three papers, published in international peer-reviewed journals. The first publication is a literature review on sensor platforms used in agricultural research. A subdivision of sensors and their applications was done, based on a detailed categorization model. It evaluates strengths and weaknesses, and discusses research results gathered with aerial and ground platforms with different sensors. Also, autonomous robots and swarm technologies suitable for PF tasks were reviewed. The second publication focuses on spectral and fluorescence sensors for BM, yield and N detection. The ground sensors were mounted on the Hohenheim research sensor platform Sensicle. A further spectrometer was installed in a fixed-wing Unmanned Aerial Vehicle (UAV). In this study, the sensors of the Sensicle and the UAV were used to determine plant characteristics and yield of three-year field trials at the research station Ihinger Hof, Renningen (Germany), an institution of the University of Hohenheim, Stuttgart (Germany). Winter wheat (Triticum aestivum L.) was sown on three research fields, with different N levels applied to each field. The measurements in the field were geo-referenced and logged with an absolute GPS accuracy of ±2.5 cm. The GPS data of the UAV was corrected based on the pitch and roll position of the UAV at each measurement. In the first step of the data analysis, raw data obtained from the sensors was post-processed and was converted into indices and ratios relating to plant characteristics. The converted ground sensor data were analysed, and the results of the correlations were interpreted related to the dependent variables (DV) BM weight, wheat yield and available N. The results showed significant positive correlations between the DVs and the Sensicle sensor data. For the third paper, the UAV sensor data was included into the evaluations. The UAV data analysis revealed low significant results for only one field in the year 2011. A multirotor UAV was considered as a more viable aerial platform, that allows for more precision and higher payload. Thereby, the ground sensors showed their strength at a close measuring distance to the plant and a smaller measurement footprint. The results of the two ground spectrometers showed significant positive correlations between yield and the indices from CropSpec, NDVI (Normalised Difference Vegetation Index) and REIP (Red-Edge Inflection Point). Also, FERARI and SFR (Simple Fluorescence Ratio) of the MP fluorescence sensor were chosen for the yield prediction model analysis. With the available N, CropSpec and REIP correlated significantly. The BM weight correlated with REIP even at a very early growing stage (Z 31), and with SAVI (Soil-Adjusted Vegetation Index) at ripening stage (Z 85). REIP, FERARI and SFR showed high correlations to the available N, especially in June and July. The ratios and signals of the MP sensor were highly significant compared to the BM weight above Z 85. Both ground spectrometers are suitable for data comparison and data combination with the active MP fluorescence sensor. Through a combination of fluorescence ratios and spectrometer indices, linear models for the prediction of wheat yield were generated, correlating significantly over the course of the vegetative period for research field Lammwirt (LW) in 2012. The best model for field LW in 2012 was selected for cross-validation with the measurements of the fields Inneres Täle (IT) and Riech (RI) in 2011 and 2012. However, it was not significant. By exchanging only one spectral index with a fluorescence ratio in a similar linear model, it showed significant correlations. This work successfully proves the combination of different sensor ratios and indices for the detection of plant characteristics, offering better and more robust predictions and quantifications of field parameters without employing destructive methods. The MP sensor proved to be universally applicable, showing significant correlations to the investigated characteristics such as BM weight, wheat yield and available N.Diese Arbeit kombiniert optische Sensoren auf einer Sensorplattform (SPF) am Boden und in der Luft bei Messungen in Weizen, um die Stickstoff-(N)-Werte zu identifizieren, während gleichzeitig die Biomasse (BM) geschätzt und der Ertrag vorhergesagt wird. Erstmals wurde hierfür der Fluoreszenzsensor Multiplex Research (MP) in Weizen eingesetzt. Die Ziele dieser Dissertation umfassen: (i) Bewertung verfügbarer Sensoren und SPF, die in der Präzisionslandwirtschaft zur Quantifizierung des Ernährungszustandes von Nutzpflanzen verwendet werden, (ii) Erfassung von Daten mit zwei Spektrometern am Boden, einem Spektrometer auf einem Modellflugzeug (UAV) und einem Fluoreszenzsensor am Boden, (iii) Erstellung effektiver Nachbearbeitungsmethoden für die Datenkorrektur, (iv) Analyse und Evaluation der Sensoren für die Abbildung der BM, des Ertrags und des N-Gehaltes in der Pflanze, und (v) Ertragssimulation als Funktion von Merkmalen unterschiedlicher Sensorsignale. Diese Arbeit enthält drei Artikel, die in international begutachteten Fachzeitschriften publiziert wurden. Die erste Veröffentlichung ist eine Literaturrecherche über SPF in der Agrarforschung. Ein detailliertes Kategorisierungsmodell wird für eine allgemeine Unterteilung der Sensoren und deren Anwendungsgebiete herangenommen, die Stärken und Schwächen bewertet, und die Forschungsergebnisse von Luft- und Bodenplattformen mit unterschiedlicher Sensorik diskutiert. Außerdem werden autonome Roboter und für landwirtschaftliche Aufgaben geeignete Schwarmtechnologien beschrieben. Die zweite Publikation fokussiert sich auf Spektral- und Fluoreszenzsensoren für die Erfassung von BM, Ertrag und N. In der Arbeit wurden die Bodensensoren auf der Hohenheimer Forschungs-SPF Sensicle und der Sensor auf dem UAV in dreijährigen Feldversuchen auf der Versuchsstation Ihinger Hof der Universität Hohenheim in Renningen für die Bestimmung von Pflanzenmerkmalen und des Ertrags eingesetzt. Auf drei Versuchsfeldern wurde Winterweizen ausgesät, und in einem randomisierten Versuchsdesign unterschiedliche N-Düngestufen angelegt. Die Sensormessungen im Feld wurden mit einer absoluten GPS Genauigkeit von ±2,5 cm verortet. Die GPS Daten des UAVs wurden mittels der Nick- und Rollposition lagekorrigiert. Im ersten Schritt der Datenanalyse wurden die Sensorrohdaten nachbearbeitet und in Indizes und Ratios umgerechnet. Die Bodensensordaten wurden analysiert, und die Ergebnisse der Korrelationen in Bezug zu den abhängigen Variablen (DV) BM-Gewicht, Weizenertrag, verfügbarer sowie aufgenommener N dargestellt. Die Ergebnisse zeigen signifikant positive Korrelationen zwischen den DVs und den Sensicle-Sensordaten. Für die dritte Publikation wurden die Sensordaten des UAV in die Auswertungen miteinbezogen. Die Analyse der UAV Daten zeigte niedrige signifikante Ergebnisse für nur ein Feld im Versuchsjahr 2011. Ein Multikopter wird als zuverlässigere Luftplattform erachtet, der mehr Präzision und eine höhere Nutzlast ermöglicht. Die Sensoren auf dem Sensicle zeigten ihren Vorteil bedingt durch einen kürzeren Messabstand zur Pflanze und eine kleinere Messfläche. Die Ergebnisse der beiden Sensicle-Spektrometer zeigten signifikant positive Korrelationen zwischen dem Ertrag und den Indizes von CropSpec, NDVI (Normalised Difference Vegetation Index) und REIP (Red-Edge Inflection Point). Auch FERARI und SFR (Simple Fluorescence Ratio) des MP-Sensors wurden für die Analyse des Ertragsvorhersagemodells ausgewählt. Mit dem verfügbaren N korrelierten CropSpec und REIP hochsignifikant. Das BM-Gewicht korrelierte bereits ab einem sehr frühen Wachstumsstadium (Z31) mit REIP und im Reifestadium (Z85) mit SAVI (Soil-Adjusted Vegetation Index). REIP, FERARI und SFR zeigten hohe Korrelationen mit dem verfügbaren N, insbesondere im Juni und Juli. Die Ratios und Signale des MP Sensors sind vor allem ab Z85 gegenüber dem BM-Gewicht hochsignifikant. Durch eine Kombination von Fluoreszenzwerten und Spektrometerindizes wurden lineare Modelle zur Vorhersage des Weizenertrags erstellt, die im Verlauf der Vegetationsperiode für das Versuchsfeld Lammwirt (LW) im Jahr 2012 signifikant korrelierten. Das beste Modell für das Feld LW im Jahr 2012 wurde für die Kreuzvalidierung mit den Messungen der Versuchsfelder Inneres Täle (IT) und Riech (RI) in den Jahren 2011 und 2012 ausgewählt. Sie waren nicht signifikant, jedoch zeigten sich durch den Austausch nur eines Spektralindexes mit einem Fluoreszenzratio in einem ähnlichen linearen Modell signifikante Korrelationen. Die vorliegende Arbeit zeigt erfolgreich, dass sich die Kombination verschiedener Sensorwerte und Sensorindizes zur Erkennung von Pflanzenmerkmalen gut eignet, und ohne den Einsatz destruktiver Methoden die Möglichkeit für bessere und robustere Vorhersagen bietet. Vor allem der MP-Fluoreszenzsensor erwies sich als universell einsetzbarer Sensor, der signifikante Korrelationen zu den untersuchten Merkmalen BM-Gewicht, Weizenertrag und verfügbarem N aufzeigte

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

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

Introduction to modern instrumentation: for hydraulics and environmental sciences

Preface Natural hazards and anthropic activities threaten the quality of the environment surrounding the human being, risking life and health. Among the different actions that must be taken to control the quality of the environment, the gathering of field data is a basic one. In order to obtain the needed data for environmental research, a great variety of new instruments based on electronics is used by professionals and researchers. Sometimes, the potentials and limitations of this new instrumentation remain somewhat unknown to the possible users. In order to better utilize modern instruments it is very important to understand how they work, avoiding misinterpretation of results. All instrument operators must gain proper insight into the working principles of their tools, because this internal view permits them to judge whether the instrument is appropriately selected and adequately functioning. Frequently, manufacturers have a tendency to show the great performances of their products without advising their customers that some characteristics are mutually exclusive. Car manufacturers usually show the maximum velocity that a model can reach and also the minimum fuel consumption. It is obvious for the buyer that both performances are mutually exclusive, but it is not so clear for buyers of measuring instruments. This book attempts to make clear some performances that are not easy to understand to those uninitiated in the utilization of electronic instruments. Technological changes that have occurred in the last few decades are not yet reflected in academic literature and courses; this material is the result of a course prepared with the purpose of reducing this shortage. The content of this book is intended for students of hydrology, hydraulics, oceanography, meteorology and environmental sciences. Most of the new instruments presented in the book are based on electronics, special physics principles and signal processing; therefore, basic concepts on these subjects are introduced in the first chapters (Chapters 1 to 3) with the hope that they serve as a complete, yet easy-to-digest beginning. Because of this review of concepts it is not necessary that the reader have previous information on electronics, electricity or particular physical principles to understand the topics developed later. Those readers with a solid understanding of these subjects could skip these chapters; however they are included because some students could find them as a useful synthesis. Chapter 4 is completely dedicated to the description of transducers and sensors frequently used in environmental sciences. It is described how electrical devices are modified by external parameters in order to become sensors. Also an introduction to oscillators is presented because they are used in most instruments. In the next chapters all the information presented here is recurrently referred to as needed to explain operating principles of instruments. Unauthenticated Download Date | 10/12/14 9:29 PM VIII Preface Chapters 1 to 4 are bitter pills that could discourage readers interested in the description of specific instruments. Perhaps, those readers trying this book from the beginning could abandon it before arriving at the most interesting chapters. Therefore, they could read directly Chapters 5 to 11, going back as they feel that they need the knowledge of the previous chapters. We intended to make clear all the references to the previous subjects needed to understand each one of the issues developed in the later chapters. Chapter 5 contributes to the understanding of modern instrumentation to measure flow in industrial and field conditions. Traditional mechanical meters are avoided to focus the attention on electronic ones, such as vortex, electromagnetic, acoustic, thermal, and Coriolis flowmeters. Special attention is dedicated to acoustic Doppler current profilers and acoustic Doppler velocimeters. Chapter 6 deals with two great subjects; the first is devoted to instruments for measuring dynamic and quasi static levels in liquids, mainly water. Methods to measure waves at sea and in the laboratory are explained, as well as instruments to measure slow changes such as tides or piezometric heads for hydrologic applications. The second subject includes groundwater measurement methods with emphasis on very low velocity flowmeters which measure velocity from inside a single borehole. Most of them are relatively new methods and some are based on operating principles described in the previous chapter. Seepage meters used to measure submarine groundwater discharge are also presented. Chapter 7 presents methods and instruments for measuring rain, wind and solar radiation. Even though the attention is centered on new methods, some traditional methods are described not only because they are still in use, and it is not yet clear if the new technologies will definitely replace them, but also because describing them permits their limitations and drawbacks to be better understood. Methods to measure solar radiation are described from radiation detectors to complete instruments for total radiation and radiation spectrum measurements. Chapter 8 is a long chapter where we have tried to include most remote measuring systems useful for environmental studies. It begins with a technique called DTS (Distributed Temperature Sensing) that has the particularity of being remote, but where the electromagnetic wave propagates inside a fibre optic. The chapter follows with atmosphere wind profilers using acoustic and electromagnetic waves. Radio acoustic sounding systems used to get atmospheric temperature profiles are explained in detail as well as weather radar. Methods for ocean surface currents monitoring are also introduced. The chapter ends with ground penetrating radars. Chapter 9 is an introduction to digital transmission and storage of information. This subject has been reduced to applications where information collected by field instruments has to be conveyed to a central station where it is processed and stored. Some insight into networks of instruments is developed; we think this information will help readers to select which method to use to transport information from field to office, by means of such diverse communication media as fibre optic, digital telephony, Unauthenticated Download Date | 10/12/14 9:29 PM Preface IX GSM (Global System for Mobile communications), satellite communications and private radio frequency links. Chapter 10 is devoted to satellite-based remote sensing. Introductory concepts such as image resolution and instrument?s scanning geometry are developed before describing how passive instruments estimate some meteorological parameters. Active instruments are presented in general, but the on-board data processing is emphasized due to its importance in the quality of the measurements. Hence, concepts like Synthetic Aperture Radar (SAR) and Chirp Radar are developed in detail. Scatterometers, altimeters and Lidar are described as applications of the on-board instruments to environmental sciences. Chapter 11 attempts to transfer some experiences in field measuring to the readers. A pair of case studies is included to encourage students to perform tests on the instruments before using them. In this chapter we try to condense our ideas, most of them already expressed throughout the book, about the attitude a researcher should have with modern instruments before and after a measuring field work. As can be inferred from the foregoing description the book aims to provide students with the necessary tools to adequately select and use instruments for environmental monitoring. Several examples are introduced to advise future professionals and researchers on how to measure properly, so as to make sure that the data recorded by the instruments actually represents the parameters they intend to know. With this purpose, instruments are explained in detail so that their measuring limitations are recognized. Within the entire work it is underlined how spatial and temporal scales, inherent to the instruments, condition the collection of data. Informal language and qualitative explanations are used, but enough mathematical fundamentals are given to allow the reader to reach a good quantitative knowledge. It is clear from the title of the book that it is a basic tool to introduce students to modern instrumentation; it is not intended for formed researchers with specific interests. However, general ideas on some measuring methods and on data acquisition concepts could be useful to them before buying an instrument or selecting a measuring method. Those readers interested in applying some particular method or instrument described in this book should consider these explanations just as an introduction to the subject; they will need to dig deeper in the specific bibliography before putting hands on.Fil: Guaraglia, Dardo Oscar. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Hidraulica. Area Hidraulica Basica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Pousa, Jorge Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Laboratorio de Oceanografía Costera y Estuarios; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin

Earth resources: A continuing bibliography, issue 46

This bibliography lists 467 reports, articles and other documents introdcued into the NASA scientific and technical information system between April 1 and June 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental cultural resources geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis