The integration of multispectral drone data from Drone-in-a-Box (DiaB) systems into data spaces for agriculture is a complex task. It requires standardized methods and supporting infrastructure. The diverse nature of multispectral data, along with the vast volume collected and the technical expertise required for its processing, presents significant challenges, particularly for agricultural applications where precise data is crucial. This research began with a literature review to identify relevant data standards and their potential applications in processing multispectral drone data. Following this, experimental field tests were conducted, where imagery was collected using a DiaB system. The data were processed using the OpenDroneMap application to explore how it can be transformed into standardized formats suitable for integration into agricultural data spaces. The objective of this study is to explore how drone-based multispectral data, when processed through open-source tools, can be made compatible with agricultural data space ecosystems. The study focuses on the roles of metadata, standards (such as INSPIRE, DCAT, and ISO standard family), vocabularies and terms (such as Agrovoc and Dublin Core), API technologies, and data formats that support structured data exchange in agriculture. The results provide practical insights and technical recommendations for making drone data more accessible and reusable. A prototype SPARQL validation query was also developed to align metadata with agricultural vocabularies. These findings support the broader vision of enabling data-driven agriculture through standardized and interoperable data infrastructure. Future research should explore how this data can be integrated into agricultural data space marketplaces and evaluate its commercial potential. Additionally, there is a need to explore how smart contracts can be implemented for automated drone data management, further facilitating seamless and fair data transactions in data space marketplaces.