Traceable Ecosystem and Strategic Framework for the Creation of an Integrated Pest Management System for Intensive Farming

The appearance of pests is one of the major problems that exist in the growth of crops, as they can damage the production if the appropriate measures are not taken. Within the framework of the Integrated Pest Management strategy (IPM), early detection of pests is an essential step in order to provide the most appropriate treatment and avoid losses. This paper proposes the architecture of a system intensive farming in greenhouses featuring the ability to detect environmental variations that may favour the appearance of pests. This system can suggest a plan or treatment that will help mitigate the effects that the identified pest would produce otherwise. Furthermore, the system will learn from the actions carried out by the humans throughout the different stages of crop growing and will add it as knowledge for the prediction of future actions. The data collected from sensors, through computer vision, or the experiences provided by the experts, along with the historical data related to the crop, will allow for the development of a model that contrasts the predictions of the actions that could be implemented with those already performed by technicians. Within the technological ecosystems in which the Integrated Pest Management systems develop their action, traceability models must be incorporated. This will guarantee that the data used for the exploitation of the information and, therefore for the parameterization of the predictive models, are adequate. Thus, the integration of blockchain technologies is considered key to provide them with security and confidence

Sensors for product characterization and quality of specialty crops—A review

This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow

A blockchain and deep neural networks-based secure framework for enhanced crop protection

The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate

AI and IoT-based concrete column base cover localization and degradation detection algorithm using deep learning techniques

Internet of Things (IoT) and Artificial Intelligence (AI) technologies are currently replacing the traditional methods of handling buildings, infrastructure, and facilities design, control, and maintenance due to their precision and ease of use. This paper proposes a novel automated algorithm for the health monitoring of concrete column base cover degradation based on IoT and the state-of-the-art deep learning framework, Convolutional Neural Network (CNN). This technique is developed for instance detection and localization of the major types of column defects. Three deep machine learning training models; namely, Resnet-50, Googlenet, and Visual Geometry Group (VGG19), with 7 different network configurations and inputs were studied and compared for their classification performance and certainty. Despite that, a few articles consider the certainty of the CNN classification results, this work investigates the certainty and employs the classification error score as a new performance measure. The results of this study demonstrated the effectiveness of the proposed defect detection and localization algorithm as it managed to read all barcodes, localize defective columns, and binary classify the condition of the concrete covers against their surrounding objects. They also showed that the VGG19 network outperformed the other addressed network models and configurations. The VGG19 network yielded a health condition classification accuracy of 100% with an RMSE of 0.33% and a maximum classification error score of 0.87 %

Ubiquitous technologies for agricultural applications

Nowadays, in order to fulfill the demands of the current market conditions, the synergy between products and services is becoming very important -- Every day, people are using more technologies being less aware of it -- Ubiquitous Technologies (UT) allows the synergy between this new products and its users in a more effective way: being unobtrusive and having information widely available, reliable and relevant -- These technologies are a useful approach for the design and development of technical systems for mission-critical applications -- Located in a near future where phenomena as global warming and climate change raise the question of how food will be produced -- This work presents a ubiquitous monitoring system for greenhouses -- The system is composed by a modular hardware platform and uses Expert Systems (ES) to support the decision making process during the data processing -- The system was designed, built and tested using two different platforms in order to validate its usability for Ubiquitous Systems (US) -- A step by step process is proposed for the data acquisition, formalization and inclusion of expert knowledge into a U

Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.info:eu-repo/semantics/publishedVersio

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Metodología multi-criterio de optimización de recursos en sistemas embebidos para implementación de algoritmos de clasificación supervisados

[ES] En la actualidad, hemos visto un aumento en el uso de los sistemas embebidos debido a su flexibilidad de instalación y su capacidad de recopilar datos por medio de sensores. Estos sistemas tienen como base la combinación entre las Tecnologías de la Información y la Comunicación (TIC), el concepto de Internet of Things (IoT) y la Inteligencia Artificial (IA). Sin embargo, muchos desarrolladores e investigadores, no realizan un proceso exhaustivo sobre la veracidad de la información que busca representar el fenómeno estudiado. Se debe tener en cuenta, que los valores obtenidos por los sensores, son una aproximación del valor real, debido a la transformación de la señal de naturaleza física hacia una eléctrica. Esto ha ocasionado que la forma de almacenar dicha información esté más orientada a la cantidad que a la calidad. En consecuencia, la búsqueda de conocimiento útil a través de los sistemas embebidos, por medio de algoritmos de aprendizaje automático, se vuelve una tarea complicada. Tomando también en consideración, que el desarrollador del dispositivo electrónico, en ocasiones, no tiene un pleno conocimiento sobre el área de estudio donde va a ser empleado el sistema. La presente tesis doctoral, propone una metodología multi-criterio de optimización de recursos en sistemas embebidos para la implementación de algoritmos de clasificación empleando criterios de aprendizaje automático. Para hacer esto, se busca reducir el ruido obtenido por el porcentaje de incertidumbre ocasionado por los sensores, mediante el análisis de criterios de acondicionamiento de la señal. Además, se ha visto que, emplear un servidor externo para el almacenamiento de datos y posterior análisis de la información, influye en el tiempo de respuesta del sistema. Por esta razón, una vez cumplida la tarea de encontrar una señal depurada, se realiza un análisis de los diferentes criterios de selección de características de los datos, que permitan reducir el conjunto almacenado, para cumplir dos funciones principales. La primera, evitar la saturación de servicios computacionales con información almacenada innecesariamente. La segunda, implementar estos criterios de aprendizaje automático dentro de los propios sistemas embebidos, con el fin de que puedan tomar sus propias decisiones sin la interacción con el ser humano. Esta transformación, hace que el sistema se vuelva inteligente, ya que puede elegir información relevante y cómo puede adaptarse a su entorno de trabajo. Sin embargo, la codificación de estos modelos matemáticos que representan los algoritmos de aprendizaje automático, deben cumplir requisitos de funcionalidad, basados en la capacidad computacional disponible en un sistema embebido. Por esta razón, se presenta una nueva clasificación de sistemas embebidos, con una novedosa taxonomía de sensores, enfocados a la adquisición y análisis de datos. Concretamente, se diseña un esquema de acoplamiento de datos entre el sensor y el sistema procesador de información, que brinda una recomendación de uso del criterio de filtrado de datos, en relación con la capacidad de recursos computacionales y la forma de envío de información dentro del sistema embebido. Este proceso se valida mediante métricas de rendimiento de sensores. Por otra parte, una vez que se tenga una base de datos adecuada, se presenta una técnica de selección de los algoritmos basados en aprendizaje supervisado, que se ajuste a los requisitos de funcionalidad del sistema embebido y a su capacidad de procesar información. Específicamente, se analizan los criterios de selección de características, prototipos y reducción de dimensionalidad que se adapten a los diferentes algoritmos de clasificación para la elección de los más adecuados