Water quality monitoring, control and management (WQMCM) framework using collaborative wireless sensor networks

Improving water quality is of global concern, with agricultural practices being the major contributors to reduced water quality. The reuse of nutrient-rich drainage water can be a valuable strategy to gain economic-environmental benefits. However, currently the tools and techniques to allow this do not exist. Therefore, we have proposed a framework, WQMCM, which utilises increasingly common local farm-scale networks across a catchment, adding provision for collaborative information sharing. Using this framework, individual sub-networks can learn their environment and predict the impact of catchment events on their locality, allowing dynamic decision making for local irrigation strategies. Since resource constraints of network nodes (e.g. power consumption, computing power etc.) require a simplified predictive model for discharges, therefore low-dimensional model parameters are derived from the existing National Resource Conservation Method (NRCS), utilising real-time field values. Evaluation of the predictive models, developed using M5 decision trees, demonstrates accuracy of 84-94% compared with the traditional NRCS curve number model. The discharge volume and response time model was tested to perform with 6% relative root mean square error (RRMSE), even for a small training set of around 100 samples; however the discharge response time model required a minimum of 300 training samples to show reasonable performance with 16% RRMS

Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

Agriculture Monitoring System Using IoT: A Review Paper

In recent years evolution of Internet of Things has brought revolution in many different sectors.  The agriculture sector has become efficient and cost effective due to role of IoT. Monitoring systems has become the crucial part of agriculture. In past few years lot of work is been done and many systems has been purposed in this regard. In this paper we are going to make comparison between 4 different agriculture monitoring systems. We are also going to discuss what type of hardware including different sensors are used in those systems. We are also going to discuss software and platform on which those applications run. We will also discuss processing mechanism and what kind of algorithms are used in these systems and in the last we are going to make a comparative table based on these parameters

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications

Architecture and communication protocol to monitor and control water quality and irrigation in agricultural environments

[ES] La introducción de soluciones tecnológicas en la agricultura permite reducir el uso de recursos y aumentar la producción de los cultivos. Además, la calidad del agua de regadío se puede monitorizar para asegurar la seguridad de los productos para el consumo humano. Sin embargo, la localización remota de la mayoría de los campos presenta un problema para proveer de cobertura inalámbrica a los nodos sensores y actuadores desplegados en los campos y los canales de agua para regadío. El trabajo presentado en esta tesis aborda el problema de habilitar la comunicación inalámbrica entre los dispositivos electrónicos desplegados para la monitorización de la calidad del agua y el campo a través de un protocolo de comunicación y arquitectura heterogéneos. La primera parte de esta tesis introduce los sistemas de agricultura de precisión (PA) y la importancia de la monitorización de la calidad del agua y el campo. Asimismo, las tecnologías que permiten la comunicación inalámbrica en sistemas PA y el uso de soluciones alternativas como el internet de las cosas bajo tierra (IoUT) y los vehículos aéreos no tripulados (UAV) se introducen también. Después, se realiza un análisis en profundidad del estado del arte respecto a los sensores para la monitorización del agua, el campo y las condiciones meteorológicas, así como sobre las tecnologías inalámbricas más empleadas en PA. Además, las tendencias actuales y los desafíos de los sistemas de internet de las cosas (IoT) para regadío, incluyendo las soluciones alternativas introducidas anteriormente, han sido abordados en detalle. A continuación, se presenta la arquitectura propuesta para el sistema, la cual incluye las áreas de interés para las actividades monitorización que incluye las áreas de los canales y el campo. A su vez, la descripción y los algoritmos de operación de los nodos sensores contemplados para cada área son proporcionados. El siguiente capítulo detalla el protocolo de comunicación heterogéneo propuesto, incluyendo los mensajes y alertas del sistema. Adicionalmente, se presenta una nueva topología de árbol para redes híbridas LoRa/WiFi multisalto. Las funcionalidades específicas adicionales concebidas para la arquitectura propuesta están descritas en el siguiente capítulo. Éstas incluyen algoritmos de agregación de datos para la topología propuesta, un esquema de las amenazas de seguridad para los sistemas PA, algoritmos de ahorro de energía y tolerancia a fallos, comunicación bajo tierra para IoUT y el uso de drones para adquisición de datos. Después, los resultados de las simulaciones para las soluciones propuestas anteriormente son presentados. Finalmente, se tratan las pruebas realizadas en entornos reales para el protocolo heterogéneo presentado, las diferentes estrategias de despliegue de los nodos empleados, el consumo energético y la función de cuantificación de fruta. Estas pruebas demuestran la validez de la arquitectura y protocolo de comunicación heterogéneos que se han propuesto.[CA] La introducció de solucions tecnològiques en l'agricultura permet reduir l'ús de recursos i augmentar la producció dels cultius. A més, la qualitat de l'aigua de regadiu es pot monitoritzar per assegurar la qualitat dels productes per al consum humà. No obstant això, la localització remota de la majoria dels camps presenta un problema per a proveir de cobertura sense fils als nodes sensors i actuadors desplegats als camps i els canals d'aigua per a regadiu. El treball presentat en aquesta tesi tracta el problema d'habilitar la comunicació sense fils entre els dispositius electrònics desplegats per a la monitorització de la qualitat de l'aigua i el camp a través d'un protocol de comunicació i arquitectura heterogenis. La primera part d'aquesta tesi introdueix els sistemes d'agricultura de precisió (PA) i la importància de la monitorització de la qualitat de l'aigua i el camp. Així mateix, també s'introdueixen les tecnologies que permeten la comunicació sense fils en sistemes PA i l'ús de solucions alternatives com l'Internet de les coses sota terra (IoUT) i els vehicles aeris no tripulats (UAV). Després, es realitza una anàlisi en profunditat de l'estat de l'art respecte als sensors per a la monitorització de l'aigua, el camp i les condicions meteorològiques, així com sobre les tecnologies sense fils més emprades en PA. S'aborden les tendències actuals i els reptes dels sistemes d'internet de les coses (IoT) per a regadiu, incloent les solucions alternatives introduïdes anteriorment. A continuació, es presenta l'arquitectura proposada per al sistema, on s'inclouen les àrees d'interès per a les activitats monitorització en els canals i el camp. Finalment, es proporciona la descripció i els algoritmes d'operació dels nodes sensors contemplats per a cada àrea. El següent capítol detalla el protocol de comunicació heterogeni proposat, així como el disseny del missatges i alertes que el sistema proposa. A més, es presenta una nova topologia d'arbre per a xarxes híbrides Lora/WiFi multi-salt. Les funcionalitats específiques addicionals concebudes per l'arquitectura proposada estan descrites en el següent capítol. Aquestes inclouen algoritmes d'agregació de dades per a la topologia proposta, un esquema de les alertes de seguretat per als sistemes PA, algoritmes d'estalvi d'energia i tolerància a fallades, comunicació per a IoUT i l'ús de drons per a adquisició de dades. Després, es presenten els resultats de les simulacions per a les solucions proposades. Finalment, es duen a terme les proves en entorns reals per al protocol heterogeni dissenyat. A més s'expliquen les diferents estratègies de desplegament dels nodes empleats, el consum energètic, així com, la funció de quantificació de fruita. Els resultats d'aquetes proves demostren la validesa de l'arquitectura i protocol de comunicació heterogenis propost en aquesta tesi.[EN] The introduction of technological solutions in agriculture allows reducing the use of resources and increasing the production of the crops. Furthermore, the quality of the water for irrigation can be monitored to ensure the safety of the produce for human consumption. However, the remote location of most fields presents a problem for providing wireless coverage to the sensing nodes and actuators deployed on the fields and the irrigation water canals. The work presented in this thesis addresses the problem of enabling wireless communication among the electronic devices deployed for water quality and field monitoring through a heterogeneous communication protocol and architecture. The first part of the dissertation introduces Precision Agriculture (PA) systems and the importance of water quality and field monitoring. In addition, the technologies that enable wireless communication in PA systems and the use of alternative solutions such as Internet of Underground Things (IoUT) and Unmanned Aerial Vehicles (UAV) are introduced as well. Then, an in-depth analysis on the state of the art regarding the sensors for water, field and meteorology monitoring and the most utilized wireless technologies in PA is performed. Furthermore, the current trends and challenges for Internet of Things (IoT) irrigation systems, including the alternate solutions previously introduced, have been discussed in detail. Then, the architecture for the proposed system is presented, which includes the areas of interest for the monitoring activities comprised of the canal and field areas. Moreover, the description and operation algorithms of the sensor nodes contemplated for each area is provided. The next chapter details the proposed heterogeneous communication protocol including the messages and alerts of the system. Additionally, a new tree topology for hybrid LoRa/WiFi multi-hop networks is presented. The specific additional functionalities intended for the proposed architecture are described in the following chapter. It includes data aggregation algorithms for the proposed topology, an overview on the security threats of PA systems, energy-saving and fault-tolerance algorithms, underground communication for IoUT, and the use of drones for data acquisition. Then, the simulation results for the solutions previously proposed are presented. Finally, the tests performed in real environments for the presented heterogeneous protocol, the different deployment strategies for the utilized nodes, the energy consumption, and a functionality for fruit quantification are discussed. These tests demonstrate the validity of the proposed heterogeneous architecture and communication protocol.García García, L. (2021). Architecture and communication protocol to monitor and control water quality and irrigation in agricultural environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17422

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment