The Rise of IoT-Powered Sensor Networks
The rapid growth of Internet of Things (IoT) technologies has led to a significant increase in the deployment of sensor networks across a wide range of industries. These sensor networks are generating terabytes of data every day, providing valuable insights into environmental conditions, plant growth, and agricultural processes. However, effectively calibrating, annotating, and aggregating this vast amount of data remains a significant challenge, especially when the data is produced in multiple locations and at different scales.
To address this challenge, researchers have developed innovative solutions like the CropSight system, a scalable and open-source information management platform that integrates high-frequency environmental data and crop images from distributed IoT sensors. By leveraging a two-component architecture, CropSight enables seamless data transfer, annotation, and centralized management, allowing users to closely monitor ongoing experiments and access historical datasets.
Designing a Scalable and Flexible IoT-Based System
The CropSight system follows a distributed systems design, where the first component is a device-side system responsible for interacting directly with distributed IoT sensing devices. This lightweight system ensures onboard data standardization, collection, and synchronization with the server-side component.
The second component is the server-side system, which is more comprehensive and responsible for managing and visualizing the collected data. Powered by PHP and MySQL, the server-side system collates and stores image- and sensor-based data, providing a unified web interface for users to oversee data collection, calibration, and storage.
Sensor Networks play a crucial role in modern agriculture, enabling continuous and precise measurement of dynamic phenotypes that are essential for understanding the relationship between plant performance, genotypes, and environmental factors.
Enabling Scalable and Flexible Deployment
To ensure scalability and flexibility, the CropSight system leverages a self-operating network in field experiments, where USB WiFi dongles are mounted on IoT devices to establish a Star or Mesh network topology. This approach enables flexible WiFi coverage over experiment sites, allowing peer-to-peer HTTP access points for data calibration and synchronization.
The Model-View-Controller (MVC) software architecture of CropSight further enhances its modularity and extensibility. By separating the internal information flows, the system enables reusable source code for both device-side and server-side software implementation, and parallel software development to add new functions while experiments are ongoing.
Comprehensive Experiment and Data Management
CropSight’s comprehensive experiment and data management features enable users to closely monitor ongoing experiments and access historical datasets. The system provides a grid view to present the geolocation of experiments, experimental layouts, and representative daily images, as well as a list view that offers detailed statistics on all monitored crops, including crop information, genotypes, and distributed phenotyping device details.
The server-side system also includes a microclimate visualization function, which allows users to view real-time sensor readings such as ambient temperature, humidity, light levels, soil temperature, and soil moisture for each monitored plot or pot. This information is crucial for understanding the relationship between environmental conditions and crop performance, enabling informed decisions in crop management and genotype-environment (GxE) studies.
Enabling Smart Agricultural Practices
The CropSight system has the potential to significantly contribute to the advancement of dynamic data collation and scalable experimental management for both plant phenotyping and crop GxE studies. By providing near real-time environmental and crop growth monitoring, as well as historical and current experiment comparison, CropSight can support sustainable agricultural practices and environmental-friendly food production.
Recent research has highlighted the importance of innovative, openly shareable solutions built on widely accessible digital infrastructures to address global food security challenges. The CropSight system, with its scalable and open-source design, offers the scientific community a range of interfacing options to adopt and extend, contributing to the development of smart agricultural practices in the near future.
Expanding Horizons: Scalability and Cloud-Based Deployment
To further enhance the scalability and accessibility of the CropSight system, moving the system to a globally accessible cloud server with cloud-enabled distributed storage is a potential future direction. This approach would remove the requirement for institutions and agricultural practitioners to maintain their own servers and storage, making the system more widely accessible.
Additionally, integrating 3G or 4G mobile data networks to key distributed nodes in the field can improve the infield network, addressing the lack of infrastructure in rural areas. The subsampling approach demonstrated in the CropSight system, where sensor data is interpolated to model environmental variation across the whole field, can also be expanded to a larger, multi-site level, truly helping to inform decision-making in crop research and agricultural practices across a country’s arable land.
By continuously enhancing the scalability, accessibility, and flexibility of the CropSight system, researchers and agricultural practitioners can leverage the power of IoT-based sensor networks to address the challenges of global food security, environmental sustainability, and precision farming in the years to come.