Sensor Spatial Planning Methodology for Optimal Coverage and Data Accuracy in Agricultural Parcels

Accurate sensor placement is critical in precision agriculture to collect high-resolution data essential for effective monitoring and decision-making. This study presents a comprehensive methodology for optimizing the spatial placement of sensors, focusing on determining the number of sensors needed and their optimal positions to ensure data quality and adequate area coverage. This methodology addresses the challenges posed by terrain restrictions, cost constraints, and data resolution needs. It is versatile, supporting in-situ monitoring, UAV-based sensing, and soil sampling for applications such as soil health analysis and soil organic carbon prediction models.

In many Research and Innovation Labs (RILs), the resolution of Earth Observation (EO) data, such as Sentinel-5 imagery with a resolution of 5×3 km, is often insufficient for the specific needs of agricultural parcels. To complement EO data, additional information must be gathered using in-situ sensors or UAVs. These additional data collection methods can provide higher resolution and more diverse data types, which are crucial for localized agricultural applications. However, the placement of sensors significantly impacts the quality and adequacy of the collected data. Dense sensor deployment across an entire area is often infeasible due to terrain challenges, budgetary limits, and the specific nature of the data being collected.

The methodology developed to address these challenges combines convex optimization, soft clustering, and cost-minimization techniques. The process begins by analyzing the statistical properties of the dataset, such as maximizing variance and maintaining the mean value, to ensure comprehensive data representation. This approach identifies key locations within the parcel that can adequately describe distributed values, reducing the need for excessive sensor deployment while maintaining data integrity.

For areas with existing spatial maps or datasets, the methodology applies weighted subsampling and soft clustering to identify optimal sensor locations. Weighted distributions prioritize critical areas for data collection, ensuring that key zones receive sufficient coverage. In cases where spatial maps are unavailable, an in-house cost-minimization algorithm guides the placement of sensors or UAVs. This algorithm incorporates factors such as terrain, accessibility, and installation costs to balance logistical constraints with data coverage requirements.

This methodology is compatible with diverse data sources, including EO data, hyperfield data, and in-situ sensor data from IoT networks. For instance, it can leverage data from soil moisture monitoring systems. Additionally, the methodology can guide soil sampling strategies for soil health assessment and serve as input for soil organic carbon prediction models. Its adaptability allows it to meet the needs of various agricultural monitoring applications, ranging from broad-scale field evaluations to detailed soil property studies.

Moreover, it enhances data quality by ensuring optimal sensor placement that captures maximum variability within the monitored area and it reduces costs and improves efficiency by minimizing the number of sensors needed. The approach is scalable and flexible, accommodating parcels of varying sizes and adapting to different data collection requirements and its integration with multiple data sources provides a comprehensive and cost-effective solution for advancing precision agriculture and sustainable resource management.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under ScaleAgData project (Grant Agreement No. 101086355).