Library of model components for process simulation relevant to production activities, Prototype 1 versions

Production Economics,

Rab-KAMS: A reproducible knowledge management system with visualization for preserving Rabbit Farming and Production Knowledge

The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production' Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users' interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users' rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security

Ontology based Approach for Precision Agriculture

In this paper, we propose a framework of knowledge for an agriculture ontology which can be used for the purpose of smart agriculture systems. This ontology not only includes basic concepts in the agricultural domain but also contains geographical, IoT, business subdomains, and other knowledge extracted from various datasets. With this ontology, any users can easily understand agricultural data links between them collected from many different data resources. In our experiment, we also import country, sub-country and disease entities into this ontology as basic entities for building agricultural linked datasets later

Ontology-based knowledge representation and semantic search information retrieval: case study of the underutilized crops domain

The aim of using semantic technologies in domain knowledge modeling is to introduce the semantic meaning of concepts in knowledge bases, such that they are both human-readable as well as machine-understandable. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based approaches have been increasingly adopted to formally represent domain knowledge. The primary objective of this thesis work has been to use semantic technologies in advancing knowledge-sharing of Underutilized crops as a domain and investigate the integration of underlying ontologies developed in OWL (Web Ontology Language) with augmented SWRL (Semantic Web Rule Language) rules for added expressiveness. The work further investigated generating ontologies from existing data sources and proposed the reverse-engineering approach of generating domain specific conceptualization through competency questions posed from possible ontology users and domain experts. For utilization, a semantic search engine (the Onto-CropBase) has been developed to serve as a Web-based access point for the Underutilized crops ontology model. Relevant linked-data in Resource Description Framework Schema (RDFS) were added for comprehensiveness in generating federated queries. While the OWL/SWRL combination offers a highly expressive ontology language for modeling knowledge domains, the combination is found to be lacking supplementary descriptive constructs to model complex real-life scenarios, a necessary requirement for a successful Semantic Web application. To this end, the common logic programming formalisms for extending Description Logic (DL)-based ontologies were explored and the state of the art in SWRL expressiveness extensions determined with a view to extending the SWRL formalism. Subsequently, a novel fuzzy temporal extension to the Semantic Web Rule Language (FT-SWRL), which combines SWRL with fuzzy logic theories based on the valid-time temporal model, has been proposed to allow modeling imprecise temporal expressions in domain ontologies

A abordagem POESIA para a integração de dados e serviços na Web semantica

Orientador: Claudia Bauzer MedeirosTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: POESIA (Processes for Open-Ended Systems for lnformation Analysis), a abordagem proposta neste trabalho, visa a construção de processos complexos envolvendo integração e análise de dados de diversas fontes, particularmente em aplicações científicas. A abordagem é centrada em dois tipos de mecanismos da Web semântica: workflows científicos, para especificar e compor serviços Web; e ontologias de domínio, para viabilizar a interoperabilidade e o gerenciamento semânticos dos dados e processos. As principais contribuições desta tese são: (i) um arcabouço teórico para a descrição, localização e composição de dados e serviços na Web, com regras para verificar a consistência semântica de composições desses recursos; (ii) métodos baseados em ontologias de domínio para auxiliar a integração de dados e estimar a proveniência de dados em processos cooperativos na Web; (iii) implementação e validação parcial das propostas, em urna aplicação real no domínio de planejamento agrícola, analisando os benefícios e as limitações de eficiência e escalabilidade da tecnologia atual da Web semântica, face a grandes volumes de dadosAbstract: POESIA (Processes for Open-Ended Systems for Information Analysis), the approach proposed in this work, supports the construction of complex processes that involve the integration and analysis of data from several sources, particularly in scientific applications. This approach is centered in two types of semantic Web mechanisms: scientific workflows, to specify and compose Web services; and domain ontologies, to enable semantic interoperability and management of data and processes. The main contributions of this thesis are: (i) a theoretical framework to describe, discover and compose data and services on the Web, inc1uding mIes to check the semantic consistency of resource compositions; (ii) ontology-based methods to help data integration and estimate data provenance in cooperative processes on the Web; (iii) partial implementation and validation of the proposal, in a real application for the domain of agricultural planning, analyzing the benefits and scalability problems of the current semantic Web technology, when faced with large volumes of dataDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

Riego inteligente: proceso de captura de datos basado en gestión del conocimiento

This paper presents the process of acquiring environmental data that feed an intelligent irrigation control system, which based on the calculation of the evapotranspiration of a crop manages to calculate the water needs of the crop to supply them. It presents the problem of irrigation because a solution based on the Internet of Things (IoT) is considered satisfactory, specifying the variables involved in the process and the characteristics of the data produced by the sensors. After this, it develops the process of capturing data on an IoT architecture based on knowledge management and with the sensing, communication, and analytical phases, referring to the R software components that have been developed to carry out this process, culminating with the projections of irrigation analytics. As irrigation is the main aspect of crop yield, a need inherent to the field sector that is not yet automated and that seeks solutions to the conditions of the Colombian countryside is supplied.El presente artículo describe el proceso de adquisición de datos medioambientales que alimentan un sistema de control de riego inteligente el cual, basado en el cálculo de la evapotranspiración de un cultivo, logra calcular las necesidades hídricas del mismo para suplirlas. Luego de plantearse la problemática del riego, y la justificación de una solución basada en Internet de las Cosas (IoT) como satisfactoria, se precisan las variables que intervienen en el proceso y las características de los datos que producen los sensores; se desarrolla el proceso de captura de datos sobre una arquitectura IoT basada en gestión del conocimiento con las fases de: sensado, comunicación y analítica, refiriendo los componentes del software R que se han implementado para realizar este proceso, culminando con las proyecciones de analítica del riego. Al ser el riego el aspecto principal del rendimiento de un cultivo se concluye que se suple una necesidad inherente al sector del campo -que aún no está automatizado- proponiéndose una solución para las condiciones específicas del campo colombiano