Search Results (550)

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

Zearalenone (ZEN), a mycotoxin produced by Fusarium species, widely contaminates grains and feed, posing a serious threat to animal and human health. Traditional physical and chemical detoxification methods face challenges, including low efficiency, high costs, and nutrient loss. In contrast, enzymatic biodegradation has emerged as a research hotspot due to its high efficiency, specificity, and environmental friendliness. Lactone hydrolase can specifically hydrolyze the lactone ring of ZEN, converting it into a low-toxicity or non-toxic degradation product, thereby demonstrating significant potential for application in ensuring the safety of food, feed, and agricultural products. In recent years, with advancements in enzyme engineering and various biological technologies, remarkable progress has been made in ZEN-degrading enzyme research. Novel and highly efficient enzyme genes have been discovered through gene mining, while directed evolution and rational design have improved catalytic efficiency and stability. Additionally, immobilization techniques and formulation optimization have enhanced industrial applicability. This review, based on practical application needs, establishes a comprehensive evaluation system integrating enzyme characteristics, modification technologies, and process applicability, aiming to provide actionable theoretical guidance for the large-scale application of biological detoxification technologies. Full article

Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD. Full article

Pesticides are essential for modern agriculture but leave harmful residues that threaten human health and ecosystems. This paper reviews key pesticide detection technologies, including chromatography and mass spectrometry, spectroscopic methods, biosensing (aptamer/enzyme sensors), and emerging technologies (nanomaterials, AI). Chromatography-mass spectrometry remains the gold standard for lab-based precision, while spectroscopic techniques enable non-destructive, multi-component analysis. Biosensors offer portable, real-time field detection with high specificity. Emerging innovations, such as nano-enhanced sensors and AI-driven data analysis, are improving sensitivity and efficiency. Despite progress, challenges persist in sensitivity, cost, and operational complexity. Future research should focus on biomimetic materials for specificity, femtogram-level nano-enhanced detection, microfluidic “sample-to-result” systems, and cost-effective smart manufacturing. Addressing these gaps will strengthen food safety from farm to table while protecting ecological balance. This overview aids researchers in method selection, supports regulatory optimization, and evaluates sustainable pest control strategies. Full article

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article

Soybean (Glycine max L.) is an essential crop for global food, feed, and industrial applications, but its production is increasingly challenged by climate change and environmental stresses. Traditional breeding and transgenic approaches have contributed to improvements in yield and quality; however, limitations in genetic diversity and regulatory hurdles for genetically modified organisms (GMOs) underscore the need for innovative strategies to address these challenges. Genome editing technologies, particularly CRISPR/Cas9, have revolutionized soybean molecular breeding by enabling precise modifications of genes related to key agronomic traits such as yield, seed composition, and stress tolerance. These advances have accelerated the development of soybean varieties with enhanced nutritional value and adaptability. Recent progress includes improvements in editing efficiency, specificity, and the ability to target multiple genes simultaneously. However, the application of genome editing remains concentrated in a few model cultivars, and challenges persist in optimizing transformation protocols, minimizing off-target effects, and validating edited traits under field conditions. Future directions involve expanding the genetic base, integrating genome editing with synthetic biology, and addressing regulatory and public acceptance issues. Overall, genome editing offers significant potential for sustainable soybean improvement, supporting food security and agricultural resilience in the face of global challenges. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

With the rapid growth of the Internet of Things, intelligent agriculture is becoming increasingly important. Traditional agricultural monitoring methods, which rely on fossil fuels and complex wiring, hinder progress. This work introduces a hybrid nanogenerator based on a rotational-swinging mechanism (RSM-HNG) that combines triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs) for efficient wind energy harvesting and smart agriculture monitoring. The parallelogram mechanism and motion conversion structure enable the stacking and simultaneous contact-separation of multiple TENG layers. Moreover, it allows the TENG and EMG units to operate simultaneously, which improves energy harvesting efficiency and extends the system’s lifespan compared to traditional disc-based friction wind energy harvesting methods. With four stacked layers, the short-circuit current of the TENG increases from 16 μA to 40 μA, while the transferred charge rises from 0.3 μC to 1.5 μC. By optimizing the crank angle, material selection, and substrate structure, the output performance of the RSM-HNG has been significantly enhanced. This technology powers a self-sustaining wireless monitoring system for temperature, humidity, an electronic clock, and road guidance. The RSM-HNG provides continuous energy for smart agriculture, animal husbandry, and environmental monitoring, all driven by wind energy. It holds great potential for regions with abundant wind resources but limited electricity access, offering valuable applications in these areas. Full article

Microneedles have emerged as a versatile technology for biosensing across biomedical domains and are increasingly being explored for other applications like agriculture. This review highlights recent advancements in the development of microneedle-based biosensors in novel areas. Biomedical applications include continuous glucose monitoring, multiplexed biomarker detection beyond glucose, and numerous recent works presenting fully integrated systems comprising microneedle arrays alongside miniaturized wearable electronics. Agricultural applications largely focus on the detection of plant growth markers, hormones, and nutrient levels. Despite significant progress, challenges remain in overcoming biofouling and electrode degradation, optimizing electrode longevity for long-term (weeks to months) in situ monitoring, and creating scalable sensor fabrication processes. Additionally, there is a need for standardized mechanical and electrical testing protocols, and guidelines specifying critical performance metrics that should be reported to facilitate accurate literature comparisons. The review concludes by outlining key opportunities for future research to address these persisting challenges. Full article

This paper reviews technological advances in precision fertilizer application from 2020 to 2025, addressing the need for a systematic synthesis of recent innovations to support agricultural sustainability. With precision fertilization critical for efficient resource use, rapid technological progress in this field has highlighted a gap in consolidated overviews of post-2020 developments. The review focuses on three core areas: device innovation, intelligent control optimization, and simulation-driven parameter refinement. Key advancements include structural improvements in fertilizer applicators (e.g., multi-segment arc and variable-diameter designs) enhancing discharge uniformity and accuracy; integration of algorithms like PSO, fuzzy logic, and RBFNN (e.g., PSO-RBF-PID reducing flow control errors) boosting control precision; and DEM/CFD simulations optimizing device parameters. These technologies, applied in scenarios from drone-based unmanned operations to automatic targeting systems, have shown potential in reducing fertilizer use and increasing crop yields. This synthesis clarifies recent progress, offering insights for green agricultural development. Note that a few pre-2020 references are included for foundational context, ensuring completeness. Full article

Under the “double security” goal of achieving both food security and ecological protection, this study explores the green and low-carbon utilization efficiency of cultivated land (GLCUECL) in the Huaihe River Ecological Economic Belt (HREEB). This study identifies the spatiotemporal evolution characteristics and trends, promoting the green, low-carbon, and sustainable utilization of arable land resources in the HREEB, thus contributing to regional and national food and ecological security. Using a global super-efficiency EBM framework that accounts for undesirable outputs, as well as the GML index, the researchers measured and decomposed the GLCUECL in 25 prefecture-level cities of the HREEB from 2005 to 2021. The Theil index and kernel density estimation were applied to analyze regional disparities and changing developmental traits. Spatial convergence and divergence were assessed using the coefficient of variation and spatial convergence models. Key findings include the following: (1) Over time, the GLCUECL in the HREEB exhibited an overall upward trend and a non-equilibrium characteristic, namely the “East Sea-river-lake Linkage Area (ESLA) > Midwest Inland Rising Area (MIRA) > Huaihe River Ecological Economic Belt (HREEB) > North Huaihai Economic Zone (NHEZ)”. The increase in the GML index of the GLCUECL is mainly attributable to a technical progress change. (2) The overall difference in the GLCUECL tends to decline, which is mainly attributable to the intra-regional differences. (3) The overall kernel density curves for the HREEB and its three sub-regions exhibited a “rightward shift” trend. Except for the expansion and polarization of the absolute difference in the GLCUECL in the NHEZ, the absolute difference in GLCUECL in other regions, such as the HREEB, ESLA, and MIRA, exhibited a decreasing trend. (4) Spatial convergence analysis revealed that only the NHEZ lacks σ-convergence, whereas all regions exhibited β-convergence. Moreover, factors such as rural economic development level, cultivated land resource endowment, agricultural subsidy policy, crop planting structure, and technological input exerted a heterogeneous effect on the change in the GLCUECL. Based on these findings, this study offers recommendations for improving GLCUECL in the HREEB. Our recommendations include the implementation of the concept of green new development, optimization of the institution supply, establishing a regional cooperation mechanism for green and low-carbon utilization of cultivated land, and formulation of differentiated paths for improving the green and low-carbon utilization efficiency of cultivated land according to local conditions. Full article

The rapid development of artificial intelligence technologies has promoted the emergence of Agriculture 4.0, where the machines participating in agricultural activities are made smart with the capacities of self-sensing, self-decision-making, and self-execution. As representative implementations of Agriculture 4.0, intelligent selective fruit harvesting robots demonstrate significant potential to alleviate labor-intensive demands in modern agriculture, where visual detection serves as the foundational component. However, the accurate detection of fruits remains a challenging issue due to the complex and unstructured nature of fruit orchards. This paper comprehensively reviews the recent progress in visual detection methods for selective fruit harvesting robots, covering cameras, traditional detection based on handcrafted feature methods, detection based on deep learning methods, and tree branch detection methods. Furthermore, the potential challenges and future trends of the visual detection system of selective fruit harvesting robots are critically discussed, facilitating a thorough comprehension of contemporary progress in this research area. The primary objective of this work is to highlight the pivotal role of visual perception in intelligent fruit harvesting robots. Full article

With the advancement of precision agriculture, variable-rate spraying (VRS) technology has demonstrated significant potential in enhancing pesticide utilization efficiency and promoting environmental sustainability, particularly in orchard applications. As a critical medium for pesticide transport, the dynamic structural characteristics of orchard canopies exert a profound influence on spraying effectiveness. This review systematically summarizes recent progress in the dynamic perception and modeling of orchard canopies, with a particular focus on key sensing technologies such as LiDAR, Vision Sensor, multispectral/hyperspectral sensors, and point cloud processing techniques. Furthermore, it discusses the construction methodologies of static, quasi-dynamic, and fully dynamic canopy modeling frameworks. The integration of canopy sensing technologies into VRS systems is also analyzed, including their roles in spray path planning, nozzle control strategies, and precise droplet transport regulation. Finally, the review identifies key challenges—particularly the trade-offs between real-time performance, seasonal adaptability, and modeling accuracy—and outlines future research directions centered on multimodal perception, hybrid modeling approaches combining physics-based and data-driven methods, and intelligent control strategies. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article