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.

Long-term intensive agriculture has contributed to soil compaction, carbon depletion, nutrient imbalances, and disruption of microbial ecological processes, collectively constraining multiple soil functions relevant to agricultural sustainability. Crop straw return is widely considered a potential strategy for alleviating these constraints. However, existing studies and reviews have often evaluated direct straw return, straw-derived biochar, and straw-based compost separately or through individual soil indicators, limiting understanding of how biomass transformation, amendment properties, and site conditions jointly shape soil responses. To address this gap, this review comparatively synthesizes the reported mechanisms, outcomes, limitations, and potential application contexts of these three strategies within a soil multifunctionality framework. The reviewed literature is characterized by substantial heterogeneity in soil type, climate, feedstock, amendment preparation, application rate, experimental duration, and management conditions; therefore, the direction, magnitude, and persistence of reported effects require context-specific interpretation. Direct straw return was often associated with changes in soil structure, labile-carbon availability, water retention, and microbial activity, although these responses varied with straw type, incorporation depth, moisture conditions, decomposition rate, and nitrogen availability. Biochar was frequently linked to carbon stabilization, sorption, nutrient retention, and pH buffering, but the magnitude of these effects varied with feedstock properties, pyrolysis conditions, application rate, soil characteristics, and climatic context. Compost was commonly associated with increases in nutrient availability and microbial activity, whereas its performance varied with maturity, raw-material composition, salinity and pathogen risks, and field management. These comparisons suggest that the three strategies should not be assumed to be functionally equivalent, although their effects may overlap and potential combinations may be beneficial under some conditions. Based on patterns identified across the reviewed literature, we synthesize an interpretive framework linking dominant soil constraints with amendment properties and targeted soil functions. This literature-derived framework is intended to organize context-dependent evidence and support adaptive straw-return management rather than provide a universal prescription. Future research should prioritize standardized soil multifunctionality indicators, long-term multi-site comparisons, and integrated assessments of agronomic benefits, carbon persistence, nutrient losses, greenhouse gas emissions, and economic feasibility.

Agricultural drip fertigation is a highly coupled dynamic process in which precision resource management depends on the coordinated performance of the entire equipment chain. Against the backdrop of global water scarcity and excessive fertilizer application, improving the full-process precision of mixing, injection, sensing, control, distribution, and terminal delivery has become a prerequisite for the wider adoption of fertigation. This review evaluates advanced process-monitoring technologies and closed-loop control architectures within modern cyber-physical fertigation systems, covering fertilizer solution preparation and mixing, injection devices, liquid- and solid-phase state sensing, intelligent control algorithms, and pipeline distribution with terminal emitters. Online mixing has evolved from gravity-based batch pre-mixing toward continuous metered injection with vortex-guided static mixing, electrical conductivity (EC) sensing with drift compensation and granular mass flow detection form the perceptual basis of closed-loop regulation, control has advanced from proportional–integral–derivative (PID) controllers through variable-universe fuzzy logic to artificial neural network (ANN) hybrids with metaheuristic optimization, and pipeline pressure regulation together with emitter anti-clogging strategies determine long-term distribution uniformity. A quantitative analysis shows that the attainable precision of the sensing–decision–execution chain is bounded by the coupling among sensor accuracy, process delays, control performance, and actuator response rather than by any single device. The review identifies five unresolved gaps—sensor reliability, multi-season field validation, interoperability, low-cost automation, and fertilizer-type adaptability—and recommends that future research prioritize low-cost Internet of Things (IoT) sensor arrays on low-power wide-area networks, edge–cloud collaborative control, and foundation-model-driven autonomous decision-making, co-designed as one coupled specification.

Garlic, a significant global specialty economic crop, is currently facing severe challenges from labor shortages and escalating production costs. Achieving full-process mechanized production is the core approach to ensuring sustainable industrial development and enhancing international competitiveness. This paper systematically reviews the research progress and application status of mechanized equipment throughout the entire crop cycle of garlic production, including seeding, field management, harvesting, and post-harvest processing and sorting. The study reveals that garlic equipment is undergoing a profound transformation from traditional mechanization to “opto-electro-mechanical integration” and intelligence. In the seeding phase, breakthroughs have been made in pneumatic precision seed-metering and machine vision-based clove bud orientation technologies, significantly improving the quality of upright planting. In field management, precise variable-rate application and targeted weeding have been preliminary realized through plant protection Unmanned Aerial Vehicle (UAV) downwash airflow field simulation (CFD) and deep learning-based image segmentation. In the harvesting phase, relying on 3D Discrete Element Method (3D-DEM) soil-cutting simulation and adaptive profile root-trimming technology, the industry is accelerating the transition from inefficient segmented harvesting to low-damage combined harvesting. In the post-harvest phase, hyperspectral imaging (HSI) and multi-label convolutional neural networks (CNNs) have been utilized to achieve high-speed non-destructive detection of internal and external quality. However, industry still faces critical bottlenecks such as the insufficient integration of machinery and agronomy, poor robustness of intelligent perception algorithms in complex environments, and high damage rates of core soil-engaging components. Future research should focus on lightweight algorithm deployment, digital twin-driven virtual prototyping, and the construction of regional standardized machinery–agronomy systems, aiming to build an efficient and universal intelligent production closed-loop for garlic.

Intensified paddy production plays a crucial role in sustaining rice productivity and food security; however, long-term high-frequency puddling, heavy machinery operations under wet soil conditions, simplified cropping systems, and insufficient organic matter inputs have progressively degraded the physical structure of paddy soils. Such structural degradation not only weakens soil water movement, nutrient supply, and aeration but also restricts rice root penetration, alters rhizosphere processes, and disrupts plant–soil feedbacks. Previous studies have largely focused on individual aspects such as soil compaction, amendment-based improvement, water management, or root responses, whereas an integrated understanding of the multi-source drivers, functional consequences, and restoration pathways of soil structural degradation in intensified paddy fields remains limited. Following the overarching theme of soil degradation and regeneration, this review systematically synthesizes the indicator framework, formation mechanisms, degradation typology, ecological consequences, and regulation strategies of paddy soil structural degradation. We further clarify the transition of degraded paddy soils from single physical constraints to the coupled decline of physical, chemical, and biological functions, and compare the agronomic performance, environmental implications, implementation feasibility, and trade-offs of different restoration pathways. Existing evidence indicates that soil structural degradation in paddy fields can impair root-zone pore connectivity, rhizosphere oxygen supply, nutrient acquisition, microbial-mediated carbon and nitrogen cycling, and greenhouse gas regulation, thereby affecting rice growth, yield stability, and the ecological sustainability of paddy systems. Accordingly, the restoration of degraded paddy soils should move beyond short-term loosening or single-factor amendment toward integrated regeneration strategies that maintain soil structural health, reconstruct plough-layer functions, enhance root–soil interactions, and promote the synergistic recovery of pore networks, aggregates, organic carbon, and microbial processes. This review provides a theoretical basis and research reference for the precise restoration of soil structural constraints and the sustainable management of plant–soil systems in intensified paddy fields.

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.