Digital Twins (DTs) are increasingly being applied in agriculture to enhance precision farming, optimize resource use, and improve sustainability. This review explores the architecture, components, and applications of DTs in agricultural production, emphasizing their role in the agricultural lifecycle, including edaphic, phytotechnologic, postharvest, and farm infrastructure stages. DTs are defined as virtual representations of real-world entities and processes, synchronized to provide insights into past, present, and future scenarios. They integrate data from sensors, IoT, and cloud computing to monitor, simulate, and optimize agricultural operations, leading to improved decision-making and resource efficiency. DTs are critical in the context of precision agriculture and Agriculture 4.0, leveraging technologies such as AI, IoT, and big data analytics. They enable real-time monitoring, predictive maintenance, and simulation of agricultural processes, supporting sustainable practices by reducing water, fertilizer, and pesticide use. The architecture of DTs includes data processing, communication technologies, cloud computing, and virtual representation through AR, VR, and MR. These technologies facilitate the integration of DTs with other emerging technologies like blockchain, enhancing their capabilities in transforming agriculture. DTs are applied across various stages of agricultural production, from soil and water management to greenhouse operations and postharvest processes. They provide benefits such as improved crop yields, optimized resource use, and enhanced food safety. However, challenges include data complexity, environmental impacts, and the need for robust cybersecurity measures. The review highlights the potential of DTs to revolutionize agriculture by enabling data-driven decisions, reducing waste, and improving sustainability. Despite these benefits, the implementation of DTs requires careful consideration of environmental and ethical implications to ensure responsible and sustainable use.Digital Twins (DTs) are increasingly being applied in agriculture to enhance precision farming, optimize resource use, and improve sustainability. This review explores the architecture, components, and applications of DTs in agricultural production, emphasizing their role in the agricultural lifecycle, including edaphic, phytotechnologic, postharvest, and farm infrastructure stages. DTs are defined as virtual representations of real-world entities and processes, synchronized to provide insights into past, present, and future scenarios. They integrate data from sensors, IoT, and cloud computing to monitor, simulate, and optimize agricultural operations, leading to improved decision-making and resource efficiency. DTs are critical in the context of precision agriculture and Agriculture 4.0, leveraging technologies such as AI, IoT, and big data analytics. They enable real-time monitoring, predictive maintenance, and simulation of agricultural processes, supporting sustainable practices by reducing water, fertilizer, and pesticide use. The architecture of DTs includes data processing, communication technologies, cloud computing, and virtual representation through AR, VR, and MR. These technologies facilitate the integration of DTs with other emerging technologies like blockchain, enhancing their capabilities in transforming agriculture. DTs are applied across various stages of agricultural production, from soil and water management to greenhouse operations and postharvest processes. They provide benefits such as improved crop yields, optimized resource use, and enhanced food safety. However, challenges include data complexity, environmental impacts, and the need for robust cybersecurity measures. The review highlights the potential of DTs to revolutionize agriculture by enabling data-driven decisions, reducing waste, and improving sustainability. Despite these benefits, the implementation of DTs requires careful consideration of environmental and ethical implications to ensure responsible and sustainable use.