Field crops change continuously across growth stages, exhibit substantial spatial heterogeneity, and must be managed within short operational windows. This study presents a structured narrative review of information sensing and intelligent monitoring from pre-sowing conditions to stand establishment, growth and yield formation, biotic stress, maturity, lodging, and harvest readiness. The literature is organized by growth stage and analyzed through a common chain of agricultural need, observable variable, sensing platform, data processing method, validation design, state interpretation, and management or equipment output. Satellite remote sensing, unmanned aerial vehicle sensing, ground and proximal sensing, field Internet of Things, machinery-mounted sensors, multisource fusion, crop models, and machine learning methods are compared according to spatial support, temporal continuity, scale matching, field robustness, transfer conditions, uncertainty, and operational applicability. The reviewed studies report crop-phenotype retrieval, field-environment characterization, and biotic-stress identification under specified conditions, whereas cross-stage state inheritance, consistent reference measurements, independent validation, and conversion of monitoring results into executable tasks remain insufficiently established. The review therefore develops a lifecycle-oriented information-processing perspective in which multisource observations are quality-marked, interpreted as stage states, linked across time and scale, and checked against management and equipment records. Future work should strengthen cross-crop and cross-region validation, mechanistic and data-driven model coordination, uncertainty reporting, interoperability, and field feedback without presuming universally autonomous decision-making.

Agricultural tractors operate at low speeds under heavy loads. Disturbances from multiple load sources and coupled power flows challenge the fuel economy, transient response, and adaptability of conventional diesel powertrains. Hybrid powertrains offer a viable route to improve energy efficiency and dynamic performance; however, their design and control must be tailored to agricultural operating conditions. This review synthesizes recent literature on hybrid tractor powertrains through an operating-condition-oriented framework. It first characterizes the power demands of traction, power take-off (PTO), transport, and hydraulic operations and compares the applicability of series, parallel, and compound hybrid architectures. It then reviews key component technologies, including engines, electric-drive systems, and energy-storage systems, with an emphasis on coordinated design and power-flow allocation. Control strategies, energy-management approaches, and powertrain parameter-matching methods are further assessed in relation to transient load compensation, energy balance, and long-duration operation. Emerging directions, including multisource load prediction, data-driven control, and intelligent energy management, are also discussed. The synthesis indicates that powertrain suitability depends on agricultural task requirements and that component sizing and energy management should be evaluated together under representative operating cycles. It examines trade-offs among operational quality, energy consumption, reliability, and cost, and identifies coordinated design and long-duration field validation as priorities for hybrid tractor development.