Mechanized production of processing tomatoes depends on coordinated operations, but crop variability and successive material transfers make performance strongly condition-dependent. This narrative review used structured, non-systematic literature identification across multidisciplinary databases and publisher platforms, supplemented by backward reference tracing through July 2026. Evidence was synthesized through crop–agronomy conditions, machine responses, operational trade-offs, validation maturity, sustainability-related outcomes, and downstream residue requirements. Four findings emerge. First, architecture, maturity synchronization, fruit mechanics, stand geometry, and water status define machine operating envelopes. Second, harvesting is governed by a persistent conflict among detachment, loss, damage, impurity content, throughput, and energy demand; comparisons of settings benefit from a common basis in cultivar, feed rate, machine configuration, and metric definition. Third, sensing and prediction studies provide a foundation for automatic execution and feedback-based adjustment, with replicated whole-machine field trials offering the next step in evaluating multistage control. Fourth, residue discharge, size reduction, collection, and densification need to be specified backward from soil-incorporation or valorization requirements. Operation-specific sustainability benefits are most directly supported for water and input efficiency, marketable-yield retention, and residue recovery, with matched assessments of energy, cost, and life-cycle impacts providing opportunities to extend these findings. Progress therefore requires crop-specific tests that report engineering benefits together with losses, adverse effects, resource use, and operating boundaries.

Allium vegetables, including Welsh onion and onion, are important seasoning and processing crops worldwide. However, their production still relies heavily on manual labor because of their complex agronomic characteristics and the limited adaptability of existing machinery. Small seed size, fragile seedlings, narrow planting spacing, considerable variation in underground harvest organs, and high requirements for product quality present substantial challenges for mechanized production. With the continuous decline in agricultural labor availability and increasing production costs, the development of precision, intelligent, and full-process mechanization systems adapted to the biological characteristics of Allium vegetables has become essential for improving production efficiency and industrial sustainability. This review summarizes recent advances in mechanized production technologies and intelligent equipment for Allium vegetables, covering agronomic foundations and planting systems, precision seeding and nursery production, automatic transplanting, intelligent field management, mechanized harvesting, and digital technology applications. Advances in seed pelleting, pneumatic precision metering, and seed physical property-based parameter optimization have improved seeding accuracy and uniformity for small-seeded crops. Automated transplanting technologies, including oriented bulb planting, paper-pot seedling transplanting, and robotic seedling picking and placement, provide promising solutions for reducing labor requirements, although improvements are still needed in seedling recognition, pickup reliability, placement accuracy, and soil-covering coordination. Recent developments in field management have shifted from single mechanical or chemical operations toward integrated precision approaches involving intelligent mechanical weeding, mulch-based weed control, soil sensor-based monitoring, model-driven irrigation and fertilization regulation, biological control, and UAV- and satellite-assisted crop monitoring. Mechanized harvesting technologies have progressed through improvements in digging, soil separation, clamping and conveying, root and leaf cutting, windrowing, and collection systems. Among these processes, precise control of digging depth, soil disturbance, clamping force, and component synchronization remains critical for improving harvesting efficiency and reducing mechanical damage. Furthermore, emerging digital technologies, including machine vision, RTK positioning, LiDAR, hyperspectral sensing, multisource sensor fusion, and machine learning, are accelerating the transition of Allium vegetable machinery toward intelligent perception, autonomous navigation, and adaptive operation. However, challenges remain, including insufficient integration between agronomic requirements and machinery design, limited robustness of perception systems under complex field conditions, unclear mechanisms of harvest damage, inadequate equipment adaptability for small-scale and hilly production areas, and the lack of unified evaluation standards. Future research should focus on the coordinated development of varieties, cultivation practices, agricultural machinery, and digital technologies, with particular emphasis on high-speed precision seeding, flexible automatic transplanting, intelligent narrow-row crop management, low-damage harvesting, and closed-loop control based on multisource agricultural information. The establishment of standardized, lightweight, modular, and intelligent mechanized production systems will provide important support for the sustainable, high-quality, and large-scale development of the Allium vegetable industry.