<p>Agriculture Technology (AgTech) integrates various technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, the Internet of Things (IoT), cloud and edge computing are applied to provide capabilities for collecting, transmitting, storing, processing, and analyzing agricultural data. Large amounts of data are gathered from multiple sources and processed in real time, enabling more effective decision-making. Data can be used to automate processes, resulting in savings on farm labor. To obtain the benefits of AgTech, data trustworthiness is essential and cyber resilience is required. Security flaws may result in farming equipment and processes being interrupted or inoperable, with significant revenue and capital losses. It is important for farmers to identify and respond to cyber incidents. Several existing works have attempted to classify AgTech technologies. However, most existing classifications are based on a limited set of characteristics and only focus on communication technologies in use or provide generic adversarial scenarios. There are real risks to data trustworthiness that impact on digital agriculture, and many of these risks are yet to be addressed. It is critical to understand the building blocks and possible components to be considered in a future security framework and their capabilities for satisfying the data confidentiality, quality, authenticity, and integrity requirements. This paper proposes a taxonomy to enable a consistent means of classifying the technologies that form the backbone of AgTech and outline the building blocks of data trustworthiness. This taxonomy consists of seven main criteria that recognize the technologies and systems (used to enable the movement of data at different stages) in terms of how it operates, its features, its benefits, and its limitations. Understanding the taxonomy enables effective implementation of anomaly detection and cryptographic methods to ensure data trustworthiness. Efficient AgTech with no security represents a serious risk to long-term sustainable smart farming and food security. Further, this paper outlines the integration of post-quantum cryptography with AgTech. The usefulness of the proposed taxonomy is demonstrated using two different types of case study related to the design and implementation of a modular security framework in real-world scenarios. The paper also identifies and discusses other important factors that impact data trustworthiness.</p>