Driven by the escalating global agricultural workforce shortage and the urgent need to meet the “Zero Hunger” mandate, the automation of harvest–transport workflows has emerged as a cornerstone of Agriculture 4.0. This paper highlights the latest research progress in multi-robot collaborative logistics scheduling across highly unstructured farming environments, underpinned by cutting-edge spatial perception and digital twin frameworks. Initially, we summarize the technological leap from conventional 2D geometric mapping to multi-modal semantic 3D reconstruction—fusing light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) imagery, and spatial data—to enable high-fidelity forward-looking predictions. The discussion then transitions to algorithmic advancements, emphasizing the shift from traditional centralized operations research to decentralized, data-driven approaches such as Multi-Agent Reinforcement Learning (MARL). We also explore micro-kinematic predictive control mechanisms and the growing integration of ecological sustainability metrics into routing models. To demonstrate practical engineering progress, multi-agent implementations are analyzed across three typical spatial settings: high-throughput continuous relays in open fields, global navigation satellite system (GNSS)-denied discrete routing in dense orchards, and close-proximity human–robot collaboration (HRC) in smart greenhouses. Finally, we identify the remaining barriers to the large-scale commercialization of Agricultural Cyber-Physical Systems (ACPS), such as the “Sim-to-Real” gap restricted by edge-computing capacities, unclosed economic loops, and HRC ethical dilemmas, offering a forward-looking roadmap for next-generation resilient agricultural networks.

Mechanized harvesting of subsurface crops has long been confronted with the critical engineering dilemmas of high damage rates and high impurity rates. Traditional taxonomic classification methods based on botanical families and genera fail to provide effective guidance for the engineering research and development of harvesting machinery. From an engineering perspective, this paper proposes a novel classification logic that categorizes subsurface crops into three major types based on their soil burial depth and physical distribution characteristics: shallow-soil clustered growth type (0–20 cm), mid-soil scattered growth type (20–40 cm), and deep-soil vertically rooted type (>40 cm). The harvesting bottlenecks of representative crops within these strata, including potato, onion, peanut, sweet potato, cassava, and yam, are systematically elucidated. Furthermore, this review provides an in-depth analysis of the current state of frontier core technologies, such as bionic drag reduction excavation, flexible multi-stage separation, microscopic discrete element method (DEM) simulation, kinematic optimization, and AI-based visual perception. This paper aims to reveal the common bottlenecks in subsurface crop harvesting and prospect future developmental trends centered on the deep integration of machinery and agronomy as well as intelligent perception and adaptation, thereby providing a solid theoretical foundation and engineering reference for the innovation of global agricultural machinery.

Climate change and the increasing frequency of extreme temperatures pose severe threats to global agricultural productivity, making the breeding of water-efficient crops a critical imperative. Originating from arid regions, foxtail millet serves as an ideal C4 model crop for elucidating plant adaptations to water deficits. Unlike previous reviews that often isolate genomic features from physiological responses, this review constructs an explicit conceptual framework integrating cross-scale defense mechanisms—mechanistically linking molecular signal transduction and post-transcriptional regulation to cellular homeostasis and field-scale yield stability. We first detail the developmental stage-specific physiological penalties of water stress and dissect proactive water-conservation strategies, including stomatal anatomical optimization, root-carbon reallocation, and dynamic rhizosphere remodeling. At the genetic level, we highlight the application of dynamic quantitative trait loci (QTL) mapping, which transcends the static limitations of conventional QTLs by capturing the spatiotemporal evolution of drought-tolerance traits across distinct developmental nodes. To bridge the gap between intrinsic genetic potential and field application, we spotlight the emerging integration of machine learning-assisted breeding and genomic prediction for the efficient evaluation of superior germplasms. Across this framework, several persistent gaps emerge: most drought-responsive genes identified in foxtail millet remain at the level of expression association without functional validation; dynamic QTL analysis remains underutilized relative to its capacity to resolve reproductive-stage drought tolerance; and ML-based genomic prediction, though demonstrated in this species, has not been integrated into operational breeding. Closing these gaps will require connecting high-throughput field phenotyping to genomic selection and deploying functionally validated editing targets in genetic backgrounds relevant to dryland production.

Rubber tracks are widely used on agricultural tractors, harvesters and lightweight field platforms due to their low ground-contact pressure, adaptability to uneven terrain and effective vibration attenuation. Under complex agricultural operating conditions with alternating soft/wet soil, straw cover, hard field ridges and gravel, tracks are subjected to combined tensioning, drive engagement, road-wheel compression, steering shear and cyclic bending, in which abrasive wear, fatigue cracking and rubber–reinforcement debonding usually develop together, critically affecting service life and reliability. Following the evidence chain of “operating condition–load–damage–life”, this review systematically summarizes the research progress on the failure mechanisms and durability of rubber tracks under complex agricultural operating conditions. It first summarizes typical service conditions and the spatial–temporal non-uniformity of track loads and the main failure modes and clarifies the many-to-many relationships between operating conditions and failures. It then analyzes damage evolution from four aspects: frictional wear and surface damage, fatigue crack initiation and propagation, rubber–reinforcement interface and composite failure, and coupling of multiple failure modes, highlighting geometric thinning, temperature rise and stiffness degradation as key feedback pathways accelerating late-stage damage. Furthermore, it reviews the scope and limitations of durability testing and damage characterization, numerical simulation (finite element, multibody dynamics and discrete element methods), fatigue and wear life prediction, and data-driven remaining useful life prediction. Finally, influencing factors are summarized from material, structural, operational and environmental aspects, together with multi-objective optimization design strategies. The analysis shows that although mature methodological systems exist in tire wear and rubber fatigue research, agricultural rubber tracks still lack a complete evidence chain connecting field load spectra, local damage and whole-track life, with distorted accelerated tests, poor cross-condition generalization and scarce field damage data as the main bottlenecks. Future research should prioritize reproducible field load spectra and unified test protocols, hierarchical accelerated testing that preserves failure mechanisms, and mechanism-constrained data-driven life prediction models, so as to support rubber track design that balances durability and soil protection across agricultural scenarios.

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life.

To address technical challenges such as equipment clogging and tuber damage during the mechanized harvesting of Xiangsha taro, this study designed a high-speed sliding-cutting device and conducted preliminary field performance evaluations. Based on the preliminary morphological baseline of Xiangsha taro and the distribution of soil penetration resistance, a multi-tooth rotary disc cutting device was developed. Kinematic and dynamic modelling indicated that a velocity ratio of 3.5–5.5 facilitate a ‘cycloidal loop’ trajectory, which theoretically reduces the potential for root disturbance by mitigating forward pushing forces. Initial field tests under specific orderly ridge conditions yielded a cutting qualification rate exceeding 96% and an estimated field capacity of 0.025 ha/h. While these results offer a preliminary technical reference for segmented harvesting equipment, the current validation is limited by the idealized row alignment of the experimental plot. Future research must evaluate the system’s adaptability to field irregularities and conduct direct controlled comparisons with commercial manual devices to fully substantiate its practical superiority.