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 for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio

Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz | Universidad Nacional de la Patagonia "san Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge. Centro de Investigaciones y Transferencia Golfo San Jorge: Sede Caleta Olivia - Santa Cruz; ArgentinaFil: Chesñevar, Carlos Iván. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

[No abstract available

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

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention.Comment: Published in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29-49, Sept. 202

Computer Vision Based Machine Learning and Deep Learning Approaches for Identification of Nutrient Deficiency in Crops: A Survey

Agriculture is a significant industry that plays a major role in a country’s sustainable environment and economic development. The global population demands increased food production with minimal losses. Nutrient deficiency is one of the major and crucial factors influencing crop production significantly. Common techniques for determining crop nutrition status are the diagnosis of plant morphology, Enzymology, chemical effects, fertilization, etc. However, the above techniques are invasive and time-consuming or infeasible while considering varied production practices in different locations, environments and climatic conditions. Computer Vision is an area of Computer Science that deals with creating Artificial Intelligence based vision systems that can use image data, process, and analyze as humans perform. Early Detection of Crop Nutrient deficiencies favors the farmers to monitor the affected crops and plan for the manure or fertilizer application, which supports to regain of the crop’s efficiency for attaining its maximum yield. Modern computer vision systems rely on Machine Learning (ML), Remote sensing, Satellite imagery, unmanned aerial vehicles (UAVs), Internet of things (IoT) based sensor devices, and Deep Learning (DL) models that use algorithms to extract required features from data. The objective of this work is to provide an overview of recent research and identify the scope of computer vision-based technologies used for identifying crop nutrient content and deficiency, find research challenges in predicting nutrient imbalance in comparison with plant diseases that show certain similar characteristics, thereby to improve crop health and production

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

Elm Farm Research Centre Bulletin 79 July 2005

Regular newletter from Elm Farm Research Centre (EFRC)covering research, technical and policy articles, views and comment