Welcome to this second edition of our ScaleAgData Newsletter! Every quarter, we'll give this 'digital floor' to our RI Labs, technology partners and, of course, end-users, to share insights and ideas with you. This edition we'll focus on our 2nd innovation area: edge processing. Stay tuned via our website & social media channels & don't hesitate to forward this newsletter or invite colleagues to register! Did you miss our 1st Newsletter? You can read it here.
In this 2nd ScaleAgData Newsletter:
Edge processing in revolutionizing agricultural data management
Edge processing in our RI Lab (RIL) Soil Health
Upcoming events & online calendar
Innovation area 2: Edge processing
In the realm of modern agriculture, the utilization of cutting-edge technologies becomes more and more indispensable. One such technology making waves in the agricultural sector is edge processing, a revolutionary approach that is reshaping the way data is collected, analysed, and used. In our ScaleAgData project, edge processing will be applied in different RIL's: crop management, soil health and dairy.
Edge processing, often referred to as edge computing, involves the processing and analysis of data closer to the source of generation, rather than relying solely on centralized data centers. This decentralized approach brings computation and data storage closer to the location where it is needed, thus reducing latency and bandwidth usage while enhancing efficiency and reliability. By performing data analysis and decision-making at the edge of the network, organizations can extract valuable insights instantaneously, enabling timely actions and responses.
The YaraSense, designed in this ScaleAgData project, is a solution designed for in-situ agriculture sensing, using the EGM EdgeSpot IoT box. The system operates autonomously in fields with MCU-based technology, efficient enough to collect, store and process data. This technology features also low power consumption, enabling the station to operate autonomously with solar panels. The YaraSense supports multiple sensor types and offers versatile computing capabilities, such as edge computing.
The system operates through three primary modules, each serving distinct functions.
The first module focuses on efficient data collection and storage. It enables the capture and storage of diverse data types, which can be directly stored in the Random Access Memory (RAM) of the MCU or in an external Ferroelectric Random Access Memory (FRAM) accessible on the EdgeSport platform. Data may come from various sensors, such as temperature and redox probe.
The second module oversees data processing, employing a versatile pipeline approach. This includes essential processing-steps such as data quality assessment, search algorithms, and correlation analysis. Advanced AI pipelines, including Tiny ML models, can be integrated to analyse data and trigger alerts using the connectivity module. The final module is dedicated to the transmission of high-quality data. It ensures that processed data is transmitted efficiently, maintaining its integrity and relevance. The YaraSense device will be deployed in theWater Productivity Labfor on-site acquisition and processing of several sensors like soil moisture, spectrometer, surface temperature, redox potential, and gas sensing nose.
Edge processing in our Soil Health Lab
In modern agricultural practices, the integration of edge processing technologies can help expedite the way soil properties are mapped and estimated in situ. By deploying processing capabilities directly on the edge, such as within the onboard capacities of UAVs, the temporal gap between data acquisition and actionable insights is significantly minimized. This accelerated timeline is paramount for agricultural decision-making, allowing farmers and agronomists to swiftly implement targeted interventions, such as adjusting irrigation or nutrient application, based on up-to-date soil information.
The immediacy of these insights empowers stakeholders to
optimize resource utilization
maximize crop yields
foster sustainable agricultural practices.
The process of edge processing in soil property mapping encompasses a multifaceted approach, starting with the preprocessing of raw hyperspectral data. This initial step involves standardization and calibration procedures to ensure the accuracy and consistency of the acquired information. Subsequently, advanced image processing techniques are employed to mosaic individual images seamlessly and identify regions of bare soil, which are crucial for accurate soil property estimation. Leveraging machine learning algorithms, such as neural networks, enables the development of predictive models that can analyze bare soil data and generate comprehensive maps of soil properties with high precision and efficiency. Edge processing may be applied to one or more of the aforementioned steps, with each processing stage reducing the time taken from data acquisition to knowledge extraction.
Furthermore, the integration of edge processing technologies also streamlines the entire data processing pipeline. By decentralizing processing tasks and performing them onboard UAVs or other edge devices, the need for extensive data transmission to central databases is greatly reduced. This decentralization not only mitigates potential bandwidth constraints but also enhances data security and privacy by minimizing exposure to external networks. Ultimately, the seamless integration of edge processing in agriculture heralds a new era of real-time decision support systems, facilitating proactive and data-driven management practices that are essential for meeting the evolving demands of global food security and sustainability.
Soil property maps
Within ScaleAgData and the RIL Soil Health, AUTh and ILVO aim to apply edge processing on the hyperspectral images collected from UAVs and UGVs to produce soil property maps of the recorded fields. The team has extensive experience in the offline processing of such data and with the development of AI/ML models and computational pipelines that can be deployed on platforms with limited processing capabilities. The goal is to examine the capacity and efficiency of edge processing in analyzing these rich hyperspectral data with the given hardware constraints.
Upcomingevents
Would you like to learn about upcoming events focused on the latest innovative data technologies for managing agricultural production and monitoring agricultural environments? Check our online calendar!
VITO is the project coordinator of the ScaleAgData consortium and will also contribute significantly to the 'product and service development', the RI Lab on Yield monitoring as well as the entire communication segment of the project. As a technology provider, VITO will bring data products and innovative solutions to merge sensor data into the data products. As communication lead, VITO will be managing the ScaleAgData website, social media channels as well as these digital newsletters. Feel free to contact us via liesbeth.poorters@vito.be.
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