An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies

Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can be reduced by optimally deploying a fleet of agricultural robots and schedule them by anticipating the human activity behaviour (state) during harvesting. In this paper, we propose a framework for spatio-temporal prediction of human pickers’ activities while they are picking fruits in agriculture fields. Here we exploit temporal patterns of picking operation and 2D discrete points, called topological nodes, as spatial constraints imposed by the agricultural environment. Both information are used in the prediction framework in combination with a variant of the Hidden Markov Model (HMM) algorithm to create two modules. The proposed methodology is validated with two test cases. In Test Case 1, the first module selects an optimal temporal model called as picking_state_progression model that uses temporal features of a picker state (event) to statistically evaluate an adequate number of intra-states also called sub-states. In Test Case 2, the second module uses the outcome from the optimal temporal model in the subsequent spatial model called node_transition model and performs “spatio-temporal predictions” of the picker’s movement while the picker is in a particular state. The Discrete Event Simulation (DES) framework, a proven agricultural multi-robot logistics model, is used to simulate the different picking operation scenarios with and without our proposed prediction framework and the results are then statistically compared to each other. Our prediction framework can reduce the so-called unproductive logistics time in a fully manual harvesting process by about 80 percent in the overall picking operation. This research also indicates that the different rates of picking operations involve different numbers of sub-states, and these sub-states are associated with different trends considered in spatio-temporal predictions

Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico

This research presents a bibliometric analysis of 106 journal and state-of-the-art articles indexed in Scopus and a systematic analysis of 83 selected papers. Areas of study are identified that include the prediction of crop yield and growth, the detection of plant diseases, and water and soil analysis related to different types of crops such as cereals (rice, barley, corn, wheat, soybeans); fruits (apple, cucumber); legumes (alfalfa, beans, peanuts); tubers, among others. Climatic variables, soil, water, topographic and edaphological conditions, and data mining techniques such as Neural Networks, Deep Learning, segmentation, association, and classification rules, among others, are examined to optimize the use of resources and make agricultural decisions based on data. In addition, the challenges and opportunities in this research area are highlighted as the future perspectives for developing advanced data mining solutions in the agricultural context. This analysis contributes to a better understanding of how data mining is transforming the farm sector academic and scientific community to drive efficiency, sustainability, and informed decision-making in food production.En esta investigación, se presenta un análisis bibliométrico de 106 artículos de revistas y estado del arte indexados en Scopus, junto con un análisis sistemático de 83 artículos seleccionados. Se identifican áreas de estudio que incluye la predicción de rendimiento y crecimiento de cultivos, la detección de enfermedades en plantas, análisis de agua y suelo, relacionados con diferentes tipos de cultivo como: cereales (arroz, cebada, maíz, trigo, soya); frutas (manzana, pepino); legumbres (alfalfa, frejol, cacahuate); tubérculos, entre otros. Se examinan variables climáticas, suelo, agua, condiciones topográficas, edafológicas y técnicas de minería de datos como, Redes Neuronales, Deep Learning, segmentación, reglas de asociación y clasificación, entre otras, para optimizar el uso de recursos y tomar decisiones agrícolas basadas en datos. Además, se destacan los desafíos y oportunidades en esta área de investigación, así como las perspectivas futuras para el desarrollo de soluciones de minería de datos avanzadas en el contexto agrícola. Este análisis contribuye a una mejor comprensión de cómo la minería de datos está transformando el sector agrícola, comunidad académica y científica, con el fin de impulsar la eficiencia, la sostenibilidad y la toma de decisiones informadas en la producción de alimentos

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

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Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table