Most of the small-arch-shed-recycling machines in China use manual disassembly and manual recycling, with low recycling efficiency and low mechanization. Therefore, this paper designs a small recovery machine for arch sheds, greatly improving the efficiency of the recycling of arch sheds, which can realize the lifting and collection of the arch shed rod and orderly recycling of the shed film. By performing univariate experimental studies in the field, on the basis of field experiments, we carried out an experimental analysis with machine speed and different soil moisture contents as the influencing factors and took the removal rate of the shed rod, the removal rate of the shed film, and the damage rate of seedlings as the test indexes. The test results show that the optimal parameter combination is a travel speed of 1.1 m/s, at which the operation effect is the best. The results show that under the optimal operation effect, the removal rate of the shed rod was 95.72%, the removal rate of the shed film was 98.63%, the seedling injury rate was only 2.11%, and the removal rate of the shed rod was only 4.01%, which met the requirements of the recovery operation of the arch shed and means that this approach is conducive to the recycling of the arch shed materials and the realization of sustainable development. In actual operation, the parameters should be adjusted according to the actual situation in the field to meet the different recovery needs of arch sheds.

The increasing automation of mechanized forage production has improved operational efficiency, but achieving consistent product quality remains challenging because material conditions evolve continuously across sequential operations. Current control strategies primarily focus on machine-level objectives, such as load regulation, throughput stabilization, and actuator response, while the linkage between operational decisions and final forage quality is still insufficiently established. Machinery-, sensor-, and stage-centered reviews chiefly catalog technologies and prediction performance, without consistently testing whether observations are representative, transferable, timely, and assigned to actionable material. This critical narrative review integrates recent evidence identified through Web of Science Core Collection and CNKI searches, supplemented by citation tracing of foundational studies. It presents a state-centered framework for smart sensing and sensing-to-control pathways throughout the mechanized forage production chain. Key intermediate states, including moisture distribution, leaf integrity, windrow geometry and linear density, mass flow, particle characteristics, bale density, and pore structure, are analyzed as the links between mechanical actions and downstream outcomes, such as drying performance, material loss, fermentation, heating risk, and storage stability. Sensing technologies based on electrical, mechanical, optical, microwave, weighing, and spectroscopic principles are evaluated from the perspectives of measurement representativeness, transferability, latency, uncertainty, and operational validity rather than prediction accuracy alone. Prediction accuracy describes agreement with a reference, whereas decision utility reflects whether a valid, representative, and timely estimate can change an operational decision and reduce downstream quality risk. The reviewed evidence indicates that practical quality-oriented automation requires rapid proxy-state feedback for immediate machine regulation combined with batch-linked quality measurements for delayed verification and model updating. Future progress depends on establishing causal machine–quality relationships, transferable sensing models, cross-stage data continuity, and production-scale validation.