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.

Post-harvest sorting is essential for converting the biological variability of chili peppers into consistent commercial grades, efficient processing, and higher market value. Rapid advances in machine vision, multimodal sensing, deep learning, and intelligent actuation are transforming sorting equipment from rule-based classifiers into integrated perception-to-execution systems. However, existing studies often evaluate isolated algorithms or components, leaving limited evidence that recognition accuracy translates into reliable sorting during continuous operation. We conducted a structured narrative review of English-language studies published from January 2008 to July 2026 using Web of Science, Scopus, AESC, and PubMed. Evidence was synthesized along a perception-to-execution chain, distinguishing pepper-specific sorting systems and component studies from cross-crop engineering evidence. Direct system-level evidence was concentrated in a small number of chili and bell pepper studies, whereas much of the technical discussion drew on apple, tomato, potato, sweet potato, and onion research. Visible, spectral, fluorescence, and multimodal features can distinguish ripeness, color grades, and surface defects, although performance remains sensitive to cultivar, illumination, pose, and dataset design. A small number of pepper sorting studies support the feasibility of integrating vision, conveying, and physical separation under specific operating conditions. Cross-crop studies inform the discussion of localization, pneumatic actuation, and system integration, but do not establish the performance of these approaches in chili pepper sorting. Reported performance is difficult to compare because studies rarely standardize latency, target association, false and missed rejections, product damage, energy use, and long-term reliability. Robust deployment therefore requires cross-batch datasets, synchronized target-level traceability, and evaluation protocols linking perception outputs to final physical destinations. This review identifies the boundaries of current pepper-specific evidence and proposes a system-level evaluation framework and research priorities for testing the transferability of cross-crop engineering approaches to pepper sorting.

Agricultural weeding robots are progressing from single-function automation toward end-to-end autonomy that integrates task planning, field navigation, weed perception, precision execution, and operational support. This review synthesises 217 sources identified through structured searches of major multidisciplinary, engineering, and Chinese literature databases, supplemented by citation tracking, followed by documented screening, evidence-use coding, technical-theme mapping, and a review-defined L1–L5 validation-coverage assessment. Three main findings emerge. First, reported technical evidence is concentrated at physical closed-loop feasibility in real agricultural environments rather than at complete production service. Among 157 studies with classifiable technical validation, 78 (49.7%) attained L3 as their highest level, whereas only one met the predefined criteria for complete production service and sustained operation with quantified supervision; that study concerned area-based mowing rather than target-level selective weeding. A separate analysis of 49 direct robotic-weeding technical studies showed the same concentration at L3. In a scenario-based boundary-robustness analysis, the number of direct studies attaining L4 or above increased from 1/49 under the strict criteria to 2/49 and 3/49 under two deliberately relaxed scenarios, while L3 remained the dominant highest level. Second, validation depth is uneven: navigation, trajectory control, and several execution modalities have stronger real-field evidence than global task planning, weed perception, fault diagnosis, and autonomous replenishment. Third, the principal system bottleneck is increasingly cross-module rather than purely algorithmic: spatial, temporal, and task-state inconsistencies can propagate through interfaces into missed or repeated treatment, crop damage, unfinished tasks, additional downtime, and human intervention. The review therefore argues for validation centred on recoverable complete production tasks, with explicit reporting of task completion, agronomic outcomes, faults and recovery, replenishment, downtime, resource use, and supervision.

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.

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.

Harvesting remains labor-intensive because delicate fruiting bodies, complex growing environments, selective harvesting demands, and interspecific morphological variation complicate mechanized and intelligent operation. A structured critical review of 200 publications synthesizes advances in mushroom harvesting from an agricultural-engineering perspective, focusing on harvesting mechanisms, machine vision, maturity assessment, three-dimensional localization, motion planning, end-effectors, damage reduction, and integrated robotic systems. Particular attention is given to how morphology, cultivation mode, harvesting requirements, and equipment configuration interact across representative species. On this basis, a conceptual cross-species adaptation framework links phenotypic traits with perception targets, contact constraints, separation mechanisms, and operational organization; its transferability remains to be validated through direct multi-species experiments. Key bottlenecks and evidence gaps include occlusion, clustered growth, variable maturity, mechanical damage, limited efficiency and robustness, and insufficient evidence for cross-site robustness and cross-species transfer. Future research should prioritize harvest-oriented phenotyping and multimodal perception, reconfigurable compliant modules, multi-stage perception–decision–execution coordination, cultivation–equipment co-design, interface standardization, virtual/digital validation, and long-duration production and economic evaluation. This review provides an integrated engineering framework for the transition from mechanized equipment to autonomous harvesting systems and for the development of efficient, low-damage, and adaptable mushroom-harvesting machinery.