Gjennomgang av presisjonslandbruk med bruk av droner for jordfuktighet estimering – Mot et mer bærekraftig landbruk

This master thesis is reviewing the latest published research on remote sensing technology in the agricultural sector, for soil moisture estimations towards a more sustainable precision agriculture. Modern, exciting new technological innovations will also be presented, along with the sustainable aspect of conventional agriculture with more precise agricultural practices. The synergy between UAS, SMC and sustainability are the focus of attention for this review thesis, as the possibilities and opportunities this can open for us can be of significant advancement in profitability and precision agriculture. As precision agriculture evolves and grows, the potential and opportunities also follow. The new field of unmanned aerial systems demonstrates this. There are several sectors the unmanned aerial vehicle is being welcomed with open arms, only within the agricultural sector, it has shown to be of great value for crop yield and biomass estimation. It takes little energy to run and operate and it can be from a green power source. As we all should move towards a more sustainable and eco-friendly lifestyle, industries, businesses and corporations are no exceptions. Agriculture is a major contributor to the climate change and environmental destruction, we should make a change to a more sustainable method of farming, with precision agriculture we are making this shift. The objective of this thesis is to contribute to the fundamental research for future implementation and introduction of remote sensing technology with a UAV. This thesis highlights these areas, to assist in closing the gap between researchers and endusers. By increasing the precision and applying inputs like artificial fertiliser and pesticides/herbicides at a correctly variable amount and time, a reduction of the inputs and the environmental disruption should follow, which results in an increase in the profitability for the farmers, and less environmental damages.Denne masteroppgaven gjennomgår den siste publiserte forskning av fjernmålings teknologi i landbrukssektoren, av jordfuktighets beregninger for ett mer bærekraftig presisjons jordbruk. Moderne spennende nye teknologiske utviklinger vil også bli presentert, sammen med det bærekraftig aspekt av konvensjonelt landbruk med mer nøyaktig jordbrukspraksis. Samarbeidet mellom UAS, SMC og bærekraft er i fokus i denne avhandlingen, som diskuterer mulighetene dette kan åpne for. Ved at presisjons jordbruk utvikler seg og vokser, følge også nye muligheter og metoder for utførelse av arbeidsoppgaver. Det nye fagfeltet av ubemannede luft systemer (UAS) demonstrerer dette. Det er flere sektorer som ønsker UAS velkommen, bare innenfor landbrukssektoren har det vist seg å være av stor verdi for vanningsanlegg planlegging og inspisering, avling og biomasse estimering. Det tar lite energi å operere og betjene systemet, energikilden kan være fornybar. Vi skal alle bevege oss mot ett mer bærekraftig og miljøvennlig livsstil, bransjer, bedrifter og selskaper er ingen unntak. Landbruket er en stor bidragsyter til klimaendringer og miljøskader, vi bør ta et skifte til en mer bærekraftig utvikling for landbruket, presisjon landbruk kan bidra med dette. Målet med denne avhandlingen er å bidra til grunnleggende forskning for fremtidig implementering og innføring av fjernmåling teknologi med en UAV. Denne oppgaven belyser disse områdene, og bidra i å lukke gapet mellom forskere og forbrukere. Ved å forbedre presisjonen på midler som kunstgjødsel eller sprøytemidler, på riktig tidspunkt med riktig mengde, vil resultere i en redusert menge utførelse av midler som vil igjen gi bonden større profittmargin, og mindre konsekvenser på miljøet

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

Hydro-Ecological Modeling

Water is not only an interesting object to be studied on its own, it also is an important component driving almost all ecological processes occurring in our landscapes. Plant growth depends on soil water content, as well is nutrient turnover by microbes. Water shapes the environment by erosion and sedimentation. Species occur or are lost depending on hydrological conditions, and many infectious diseases are water-borne. Modeling the complex interactions of water and ecosystem processes requires the prediction of hydrological fluxes and stages on the one side and the coupling of the ecosystem process model on the other. While much effort has been given to the development of the hydrological model theory in recent decades, we have just begun to explore the difficulties that occur when coupled model applications are being set up

Optimisation of small-scale aquaponics systems using artificial intelligence and the IoT: Current status, challenges, and opportunities

Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies

Effects of Land Use on the Ecohydrology of River Basin in Accordance with Climate Change

Water deficit affects various regions of the world. Effective approach can be based on ecohydrological solutions and the design of blue–green infrastructure. In our scientific book, we focused on papers that consider water management and adaptation of urban and rural development areas to the progressive climate change. The Special Issue includes a drought-prone place (valleys in Mexico City), reflections on the state and water resources in Lithuania, and engineering and technical articles from China and Poland. In addition, one chapter is dedicated to grassland protection in mountainous areas

Machine learning and computational chemistry to improve biochar fertilizers : a review

Traditional fertilizers are highly inefficient, with a major loss of nutrients and associated pollution. Alternatively, biochar loaded with phosphorous is a sustainable fertilizer that improves soil structure, stores carbon in soils, and provides plant nutrients in the long run, yet most biochars are not optimal because mechanisms ruling biochar properties are poorly known. This issue can be solved by recent developments in machine learning and computational chemistry. Here we review phosphorus-loaded biochar with emphasis on computational chemistry, machine learning, organic acids, drawbacks of classical fertilizers, biochar production, phosphorus loading, and mechanisms of phosphorous release. Modeling techniques allow for deciphering the influence of individual variables on biochar, employing various supervised learning models tailored to different biochar types. Computational chemistry provides knowledge on factors that control phosphorus binding, e.g., the type of phosphorus compound, soil constituents, mineral surfaces, binding motifs, water, solution pH, and redox potential. Phosphorus release from biochar is controlled by coexisting anions, pH, adsorbent dosage, initial phosphorus concentration, and temperature. Pyrolysis temperatures below 600 °C enhance functional group retention, while temperatures below 450 °C increase plant-available phosphorus. Lower pH values promote phosphorus release, while higher pH values hinder it. Physical modifications, such as increasing surface area and pore volume, can maximize the adsorption capacity of phosphorus-loaded biochar. Furthermore, the type of organic acid affects phosphorus release, with low molecular weight organic acids being advantageous for soil utilization. Lastly, biochar-based fertilizers release nutrients 2–4 times slower than conventional fertilizers

Agroforestry and Sustainable Agricultural Production

This book focuses on the potential of agroforestry to maximize agriculture production while minimizing negative effects on the environment. It collects several studies on agroforestry systems from around the world, including a variety of types of agroforestry systems, from traditional wood-pastures to tropical cocoa-based systems, and research approaches, from literature reviews to state-of-the-art ecological-economic models. The book highlights the potential of agroforestry as a promising approach for the creation of multifunctional landscapes able to face contemporary environmental challenges

Mathematical Modeling of Water Quality: Streams, Lakes and Reservoirs

This book is the first to deal comprehensively with the subject of mathematical modeling of water quality in streams, lakes, and reservoirs. About one third of the book is devoted to model development processes -- identification, formulation, parameter estimation, calibration, sensitivity testing, and application -- and a thorough review of the mathematical principles and techniques of modeling. Emphasis is placed on well documented models, representative of the current state of the art, to illustrate capabilities and limitations for the simulation of water quality. About two thirds of the book deals with specific applications of models for simulation of water quality in natural water bodies. Topics covered include modeling of temperature, dissolved oxygen and phytoplankton growth in streams, development and application of one-dimensional models of stratified impoundments, two- and three-dimensional modeling of circulation and water quality in large lakes, thermally stratified plumes and cooling ponds, ecology of lakes and reservoirs, modeling of toxic substances, and the use of models in water quality management and decision making

Water Quality Modelling Using Multivariate Statistical Analysis and Remote Sensing in South Florida

The overall objective of this dissertation research is to understand the spatiotemporal dynamics of water quality parameters in different water bodies of South Florida. Two major approaches (multivariate statistical techniques and remote sensing) were used in this study. Multivariate statistical techniques include cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), discriminant analysis (DA), absolute principal component score-multiple linear regression (APCS-MLR) and PMF receptor modeling techniques were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, a 15-year (2000–2014) data set of 12 water quality variables, and about 35,000 observations were used. Agglomerative hierarchical CA grouped 16 monitoring sites into three groups (low pollution, moderate pollution, and high pollution) based on their similarity of water quality characteristics. DA, as an important data reduction method, was used to assess the water pollution status and analysis of its spatiotemporal variation. PCA/FA identified potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules, and causes were explained. The APCS-MLR and PMF models apportioned their contributions to each water quality variable. Also, the bio-physical parameters associated with the water quality of the two important water bodies of Lake Okeechobee and Florida Bay were investigated based on remotely sensed data. The principal objective of this part of the study is to monitor and assess the spatial and temporal changes of water quality using the application of integrated remote sensing, GIS data, and statistical techniques. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of a waterbody and observed data. The developed MLR models appeared to be promising for monitoring and predicting the spatiotemporal dynamics of optically active and inactive water quality characteristics in Lake Okeechobee and Florida Bay. It is believed that the results of this study could be very useful to local authorities for the control and management of pollution and better protection of water quality in the most important water bodies of South Florida