Abstract: Smart sensors can serve as one of the most important components in the perception layer in smart agriculture. The critical value can be expected for the smart sensors in the complex agricultural scenarios, such as crop cultivation, livestock farming, and aquaculture, due to their high sensing accuracy, environmental adaptability, and operational reliability. A high demand can be placed on the real-time capability, precision, and multidimensionality of data acquisition, particularly with the global transition toward digital and intelligent agriculture. However, conventional sensors cannot fully meet the requirements for the complex agricultural environments, due to their single functionality, weak anti-interference, and low data fusion efficiency. In contrast, smart sensors can be integrated with IoT and edge computing, thereby enabling synchronous multi-source data collection and adaptive processing. In this review, a systematic review was proposed on the technological advancements and challenges in smart agricultural sensors under different application scenarios. 1) In crop farming, the smart sensors were primarily used for the in-situ monitoring of the soil moisture, microclimate parameters, and crop physiological data, in order to realize the real-time management of agricultural practices. The sensors were used to optimize the water usage and then monitor the plant health for the crop yields, thus providing for accurate data on the soil and environmental factors. 2) In the livestock sector, the integrating sensors were focused on environmental monitoring and tracking the health of the individual animals. These sensors were utilized to detect the vital signs, such as the heart rate, body temperature, and movement. Early disease detection was also facilitated to optimize the feeding practices for animal welfare. 3) In aquaculture, smart sensors were used for the continuous monitoring of the water quality. The key parameters were tracking, such as the oxygen levels, pH, and ammonia content. The behavior of the aquatic species was also monitored for a healthy and productive environment. Furthermore, the trends were also predicted in the AI-powered sensors. The future direction was then outlined for smart agriculture, in order to shift from experience to data-driven practices. Smart agricultural sensors were set to enable the large-scale applications of precision farming in sustainable agriculture. There were promising advancements in various technologies. Furthermore, several obstacles still hinder the widespread deployment of smart agricultural sensors. Major challenges included the limited environmental adaptability, long-term stability, and weak resistance to environmental interference under complex agricultural conditions. Additionally, the global insights were often required to effectively integrate the multi-source, heterogeneous data into agricultural systems. The large-scale implementation was also confined to the insufficient energy supply, network coverage, and data security risks. The current technical challenges were summarized as follows: 1) Sensing materials and device structures were required to solve the insufficient environmental adaptability and the long-term stability under variable agricultural conditions; 2) Advanced energy supply was limited to the multi-modal energy harvesting and low-power system; 3) The hardware-algorithm optimizations were coordinated to reduce the spatial resolution, signal coupling interference, and manufacturing complexity during multi-sensor integration. And 4) the large-scale deployment was also hindered by the high manufacturing costs and complex maintenance procedures. Future breakthroughs were also expected to drive the self-sustaining sensor networks with full-lifecycle, maintenance-free operation. Ultimately, the intelligent perception was established to span crop cultivation, livestock management, and ecological monitoring. In the long term, the smart agricultural sensor can hold the potential to enhance the overall efficiency, sustainability, and resilience of agricultural practices, in order to make more informed decisions on the optimal resource use. Environmental impacts can also be reduced—ultimately accelerating the transformation of agriculture toward greater automation and precision.