A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Overview of topics and questions to be addressed by the FG Mainstreaming Precision Farming : starting paper for FG meeting held on 3 and 4 June 2014

Precision farming is an innovation in agriculture allowing the right treatment of crops and livestock at the right time and smallest scale possible. It requires a seamless integration of different technologies and intelligence. Optimization of treatments at the lowest scale possible will improve yields and resource efficiency in agri-food chains, so reducing the agricultural footprint. More and more, precision farming will become the ‘licence to produce’ for farmers in the EU

A machine learning-remote sensing framework for modelling water stress in Shiraz vineyards

Thesis (MA)--Stellenbosch University, 2018.ENGLISH ABSTRACT: Water is a limited natural resource and a major environmental constraint for crop production in viticulture. The unpredictability of rainfall patterns, combined with the potentially catastrophic effects of climate change, further compound water scarcity, presenting dire future scenarios of undersupplied irrigation systems. Major water shortages could lead to devastating loses in grape production, which would negatively affect job security and national income. It is, therefore, imperative to develop management schemes and farming practices that optimise water usage and safeguard grape production. Hyperspectral remote sensing techniques provide a solution for the monitoring of vineyard water status. Hyperspectral data, combined with the quantitative analysis of machine learning ensembles, enables the detection of water-stressed vines, thereby facilitating precision irrigation practices and ensuring quality crop yields. To this end, the thesis set out to develop a machine learning–remote sensing framework for modelling water stress in a Shiraz vineyard. The thesis comprises two components. Component one assesses the utility of terrestrial hyperspectral imagery and machine learning ensembles to detect water-stressed Shiraz vines. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) ensembles were employed to discriminate between water-stressed and non-stressed Shiraz vines. Results showed that both ensemble learners could effectively discriminate between water-stressed and non-stressed vines. When using all wavebands (p = 176), RF yielded a test accuracy of 83.3% (KHAT = 0.67), with XGBoost producing a test accuracy of 80.0% (KHAT = 0.6). Component two explores semi-automated feature selection approaches and hyperparameter value optimisation to improve the developed framework. The utility of the Kruskal-Wallis (KW) filter, Sequential Floating Forward Selection (SFFS) wrapper, and a Filter-Wrapper (FW) approach, was evaluated. When using optimised hyperparameter values, an increase in test accuracy ranging from 0.8% to 5.0% was observed for both RF and XGBoost. In general, RF was found to outperform XGBoost. In terms of predictive competency and computational efficiency, the developed FW approach was the most successful feature selection method implemented. The developed machine learning–remote sensing framework warrants further investigation to confirm its efficacy. However, the thesis answered key research questions, with the developed framework providing a point of departure for future studies.AFRIKAANSE OPSOMMING: Water is 'n beperkte natuurlike hulpbron en 'n groot omgewingsbeperking vir gewasproduksie in wingerdkunde. Die onvoorspelbaarheid van reënvalpatrone, gekombineer met die potensiële katastrofiese gevolge van klimaatsverandering, voorspel ‘n toekoms van water tekorte vir besproeiingstelsels. Groot water tekorte kan lei tot groot verliese in druiweproduksie, wat 'n negatiewe uitwerking op werksekuriteit en nasionale inkomste sal hê. Dit is dus noodsaaklik om bestuurskemas en boerderypraktyke te ontwikkel wat die gebruik van water optimaliseer en druiweproduksie beskerm. Hyperspectrale afstandswaarnemingstegnieke bied 'n oplossing vir die monitering van wingerd water status. Hiperspektrale data, gekombineer met die kwantitatiewe analise van masjienleer klassifikasies, fasiliteer die opsporing van watergestresde wingerdstokke. Sodoende verseker dit presiese besproeiings praktyke en kwaliteit gewasopbrengs. Vir hierdie doel het die tesis probeer 'n masjienleer-afstandswaarnemings raamwerk ontwikkel vir die modellering van waterstres in 'n Shiraz-wingerd. Die tesis bestaan uit twee komponente. Komponent 1 het die nut van terrestriële hiperspektrale beelde en masjienleer klassifikasies gebruik om watergestresde Shiraz-wingerde op te spoor. Die Ewekansige Woud (RF) en Ekstreme Gradiënt Bevordering (XGBoost) algoritme was gebruik om te onderskei tussen watergestresde en nie-gestresde Shiraz-wingerde. Resultate het getoon dat beide RF en XGBoost effektief kan diskrimineer tussen watergestresde en nie-gestresde wingerdstokke. Met die gebruik van alle golfbande (p = 176) het RF 'n toets akkuraatheid van 83.3% (KHAT = 0.67) behaal en XGBoost het 'n toets akkuraatheid van 80.0% (KHAT = 0.6) gelewer. Komponent twee het die gebruik van semi-outomatiese veranderlike seleksie benaderings en hiperparameter waarde optimalisering ondersoek om die ontwikkelde raamwerk te verbeter. Die nut van die Kruskal-Wallis (KW) filter, sekwensiële drywende voorkoms seleksie (SFFS) wrapper en 'n Filter-Wrapper (FW) benadering is geëvalueer. Die gebruik van optimaliseerde hiperparameter waardes het gelei tot 'n toename in toets akkuraatheid (van 0.8% tot 5.0%) vir beide RF en XGBoost. In die algeheel het RF beter presteer as XGBoost. In terme van voorspellende bevoegdheid en berekenings doeltreffendheid was die ontwikkelde FW benadering die mees suksesvolle veranderlike seleksie metode. Die ontwikkelde masjienleer-afstandwaarnemende raamwerk benodig verder navorsing om sy doeltreffendheid te bevestig. Die tesis het egter sleutelnavorsingsvrae beantwoord, met die ontwikkelde raamwerk wat 'n vertrekpunt vir toekomstige studies verskaf.Master

Cyber-Agricultural Systems for Crop Breeding and Sustainable Production

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development

Programación lineal para el análisis y la recreación virtual de episodios históricos: la distribución de la artillería durante el sitio de Bilbao en 1874

[EN] The current digital technologies development makes it possible to apply new forms of studying historical events considering the geographical point of view. They rely on the location and the relationships among the different elements that took part in them over a recreated space (e.g. relief, roads, rivers); once these elements have been laid out on the virtual space, Geographic Information Systems (GIS) can be used to analyse several factors, such as distances, visibility, connectivity and so on. Nevertheless, the development of the actions was also driven by the aims, needs and beliefs (either wise or misguided) of the people/actors involved in those situations; therefore, some ways of including reasoning would significantly improve the actual recreation and understanding of the episodes. In this sense, “linear programming” is a very versatile tool for system modelling and optimization that is broadly used in many fields (e.g. industry, transports, agriculture, etc.). Likewise, this technique can also be applied to past scenarios to simulate dynamics and cross-check sources. In this text, two models regarding the distribution and the allocation of supplies during the siege of Bilbao, in the framework of the Third Carlist War (1872-1876), from both parties —beleaguerer and besieged— were established based on the war front textual reports. In these models, the scenario is recreated through the system variables (which define the alternatives that can be or could have been taken) and the constraints (which limit the range of action); moreover, the actors’ goals that guided the course of events are defined by the objective. Despite the simplification in the modelling, the results show very interesting hints about the dynamics involved during the processes and are able to highlight some critical issues that significantly conditioned the final results. Besides, the modelling process itself proved to be an opportunity for collaboration between historians and computer scientists.[ES] El desarrollo de las tecnologías digitales ha posibilitado nuevas formas de estudio de los sucesos históricos desde la perspectiva geográfica. Estos métodos se basan en la localización (sobre un espacio que incluye el relieve, las vías de comunicación, los ríos, etc.) y el establecimiento de las relaciones entre los diferentes elementos que intervinieron en dichos sucesos. Una vez que toda esta información ha sido representada en el espacio virtual, es posible recurrir a los Sistemas de Información Geográfica (SIG) con el fin de analizar diversos factores como las distancias, la visibilidad, la conectividad, etc. Sin embargo, resulta evidente que el desarrollo de los acontecimientos también estuvo condicionado por las intenciones, las necesidades y las impresiones (tanto correctas como equivocadas) de las personas que intervinieron en ellos; por lo tanto, resulta oportuno pensar que la recreación del desarrollo de los eventos históricos, así como su correcta comprensión, mejorará sustancialmente si se incorpora algún método para simular el razonamiento de los actores. En esta línea, la “programación lineal” es una opción versátil para el modelado y la optimización de sistemas que cuenta con una amplia experiencia en diversos campos como la industria, los transportes, la agricultura, etc. Asimismo, esta técnica de modelado también es aplicable a escenarios históricos con el fin de realizar simulaciones de las dinámicas que se establecieron y como método de validación de las fuentes. En el presente texto, se desarrollan —con base a los informes del frente de guerra— dos modelos relativos a la distribución de suministros durante el sitio de la villa de Bilbao —que tuvo lugar en el contexto de la Tercera Guerra Carlista (1872-1876)— que corresponden a ambas partes (es decir, a los sitiadores y a los sitiados). En los modelos, el escenario se recrea a través de las variables del sistema (las cuales definen las alternativas que pueden tomarse) y las restricciones (que limitan el rango de acción), por otro lado, las metas que guiaron el curso de los acontecimientos se definen mediante el objetivo. A pesar de la simplificación que implica el proceso de modelado, los resultados ofrecen interesantes indicaciones sobre las dinámicas que intervinieron en el desarrollo de los procesos y son capaces de identificar aspectos críticos que, efectivamente, condicionaron los resultados finales. Asimismo, el propio proceso de modelado resulta ser una oportunidad de colaboración entre historiadores y expertos informáticos. The participation of Gorka Martín and Jaione Korro in this research is supported by the Basque Government through grants for doctoral studies of the call 2019-2020. 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Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas