Task-Driven Design and Integration of Sensor Chips and Edge Computing for Smart Agriculture

Agricultural sensing systems must convert variable soil, plant and product signals into reliable information under constraints on energy, response time and maintenance. This review presents a task-driven framework linking sensor chips, analog readout, edge computing and system integration across crop production and agricultural product testing. It covers electrochemical and biochemical sensors, microfluidic and MEMS devices, optical and spectral systems, edge processors and accelerators, and board-level to heterogeneous integration. The synthesis identifies three recurring design patterns. Soil nutrient monitoring depends strongly on sensor selectivity, reference stability and calibration. Image-based plant phenotyping requires coordinated acquisition, data movement and computing. In low-abundance pathogen detection, sample handling and signal acquisition may dominate the workflow before inference performance becomes decisive. These patterns guide hardware specialization toward the dominant measurement or processing constraint. The synthesis also identifies complete-system energy, long-term stability and field validation as priorities for translating laboratory demonstrations into agricultural applications. We propose task-specific hardware configurations and evaluation criteria covering real-sample error, end-to-end response time, measured system energy, data completeness, calibration retention and life-cycle cost. Together, these outputs provide practical guidance for aligning hardware selection and integration with agricultural measurement needs.