Scutelnic Dumitru, Muradore Riccardo, Daffara Claudia
Department of Computer Science, University of Verona, Italy.
Department of Engineering for Innovation Medicine, University of Verona, Italy.
HardwareX. 2024 Oct 21;20:e00596. doi: 10.1016/j.ohx.2024.e00596. eCollection 2024 Dec.
Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS-NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.
多光谱成像（MSI）是一种用于检测不同领域材料特性的技术，涵盖从工业到医学、文化遗产，以及最近的精准农业等领域。尽管已有多种多光谱成像解决方案可供商业使用，但研究界仍在努力从性能和成本方面优化多光谱相机。农业领域的系统通常非常紧凑，并与无人机结合用于大面积数据采集。在这项工作中，我们详细介绍了一种创新的、模块化且低成本的多光谱相机解决方案的实现，该方案基于光学（可见光 - 近红外）和热（长波红外）范围内的三个核心相机系统。多光谱成像通过一个装有可互换带通滤波器的旋转轮来进行。该系统还配备了一组环境传感器，用于获取周围环境的一氧化碳浓度值、光照强度、温度和相对湿度。该技术和测量协议在实验室和野外进行了实验验证。相对于安装在无人机上的现有多光谱相机，其优势在于在特写成像设置中同时进行反射率和热发射波段的集成成像以及使用环境传感器。从多光谱图像堆栈中可以获取植物的光谱特征，并可计算各种植被指数（如归一化差异植被指数、归一化差异红边指数）以研究植物的健康状况，而热成像则提供额外的监测。特写多光谱成像有望通过获取单株植物的高质量数据集来应对精准农业的新挑战。
