Search Results (4,527)

Global population growth and the demand for sustainable food systems have pushed microalgae into the spotlight as promising novel food resources. They are rich in protein, omega-3 fatty acids, and bioactive pigments including astaxanthin and phycocyanin. Unlike conventional farming, microalgae cultivation can be conducted on non-arable land and may reduce direct competition with conventional food crops for land resources, depending on the production system used. Regulatory progress in China, the European Union (EU), and the United States has resulted in the authorization or approval of several microalgal species and microalgae-derived ingredients for specific food and nutritional applications, including dietary supplements, infant nutrition products, and alternative protein ingredients. Despite these advances, broader commercial adoption remains constrained by several challenges, such as off-flavors and the dark green color, high production costs from closed photobioreactors and energy-intensive downstream purification, fragmented regulatory frameworks across jurisdictions and limited long-term data on bioavailability, allergenicity, safety, and dose–response relationships for some emerging strains. This review focuses on microalgae as novel food resources, covering regulatory approvals, strain selection, high-value utilization, and market translation, synthesizes evidence on nutritional evaluation, application scenarios, and global regulatory differences, analyzes key bottlenecks, and proposes pathways to bridge fundamental research with industrial practice. It also highlights unresolved knowledge gaps to guide future research and policy. Full article

Globally, Trichogramma Westwood (Hymenoptera: Trichogrammatidae) is known as the most effective biological control agent due to its ability to parasitize insect pest eggs. However, identifying an appropriate host is vital for Trichogramma to prosper. Therefore, this study delves into the complex role of semiochemicals in shaping the host-seeking behavior of Trichogramma parasitoids, with a particular focus on their responses to both plant-derived and host-derived cues. The mechanism of semiochemical reception in Trichogramma wasps relies on a highly specialized, sensitive olfactory and gustatory system to locate host eggs and mates. Semiochemicals, which mediate ecological interactions, have been identified as pivotal in influencing the parasitic efficiency of Trichogramma species. Trichogramma’s host-seeking behavior is influenced not solely by ovipositional cues but also by the intrinsic physical attributes of Lepidopteran hosts, such as the scales on the wings and abdomen, which emit semiochemicals capable of eliciting positive chemotactic responses, thereby guiding parasitoids toward optimal sites for oviposition. Furthermore, the interplay between insect-derived and plant-derived chemical cues exhibits a synergistic effect, collectively enhancing the chemotactic attraction of Trichogramma, thereby fine-tuning its host-seeking behavior with greater precision and specificity. This study further underscores Trichogramma’s innate behavioral ability to discriminate between host eggs of varying developmental stages, facilitating the precise identification and selection of the most suitable host for parasitization. Age and experience both make Trichogramma more selective of hosts, but younger parasitoids may take a broader approach to host selection due to their greater life expectancy. Furthermore, the removal of these cues affects their host localization and learning abilities. Associative learning enables Trichogramma to exhibit flexible behaviors, providing them with a selective advantage; allows them to explore various hosts; and reduces environmental uncertainty. Plant structure, host density, and host age are the key factors that significantly influence the foraging and parasitism of Trichogramma. The searching speed of this parasitoid is significantly influenced by temperature. Heat stress increases VOC emissions in plants such as potato via stomatal opening, reducing herbivore attraction and enhancing parasitoid recruitment. Furthermore, air pollution, including CO₂, O₃, and NOx, impairs parasitoid efficiency by disrupting volatile-mediated host location and reducing biological control performance. Trichogramma wasps are generally effective biological control agents, but their success depends on the species used, target pest, crop, release density, and field conditions. Overall, species such as T. ostriniae, T. japonicum, and T. leucaniae show the strongest performance in several crops by increasing parasitism, reducing pest damage, and improving yield. This study highlights the successful integration of semiochemical cues in pest management programs and the effective utilization of Trichogramma in conjunction with entomopathogenic bacteria to control Lepidopteran pests. This approach contributes to the development of more effective pest management strategies, thereby promoting agricultural sustainability. Full article

Increasing pressure on surface water resources in intensive agricultural regions has driven the search for sustainable alternatives for irrigation supply, especially in areas where water quality limits crop safety and export opportunities. In this context, riverbank filtration (RBF) systems offer a nature-based solution by utilizing physical, chemical, and biological processes associated with river–aquifer exchange. However, their implementation depends on suitable site selection supported by hydrogeological, geomorphological, and hydraulic criteria. This study developed an integrated methodology to identify zones with potential for implementing RBF systems in the Roldanillo–Unión–Toro irrigation district, located in northern Valle del Cauca, Colombia. This region requires irrigation water over 10,256 ha of agricultural land (mainly sugarcane, maize, grapes, and guava). We combined geophysical methods (vertical electrical soundings, 2D electrical resistivity tomography, and passive seismic), geotechnical methods (CPTu tests), and hydraulic characterization of the river reach to evaluate subsurface stratigraphy, preliminary hydrogeological suitability, inferred river–aquifer connectivity conditions, and channel stability. The evaluation covered four sectors along an approximately 21 km stretch of the Cauca River’s left-bank alluvial valley. The results revealed pronounced lateral and vertical heterogeneity of alluvial materials. However, the “El Palmar” sector was identified as the best-supported priority sector for future RBF validation, due to the presence of profile-scale evidence of potentially permeable sandy and gravelly units with intermediate resistivity values (52–61 Ω·m), favorable stratigraphic organization, and stable river-reach conditions during the field campaign. In contrast, the other three sectors (La Esperanza, Candelaria, and Cayetana) showed more fine-grained sediments with deeper permeable strata. River-flow measurements during the July 2025 field campaign indicated high discharge conditions at the evaluated reach, while river-channel observations showed active fine-sediment transport; these findings provide hydraulic and sedimentary context for the future evaluation of induced infiltration and potential clogging, but do not constitute direct evidence of river–aquifer exchange. This study highlights the value of integrated screening approaches for prioritizing candidate RBF sites in agricultural alluvial settings, while indicating that pumping tests, piezometric monitoring, hydraulic-gradient analysis, and water-quality validation remain necessary before engineering implementation. Full article

Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-m evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field’s baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture. Full article

The Loess Hilly Region of northern Shaanxi is one of the most erosion-prone areas in the world due to its porous, erodible loess, steep slopes, and seasonal rainfall. To address this, conversion of sloping farmland to terraces has been extensively conducted across China’s loess regions, as terracing can reduce soil and water loss and enhance soil fertility. However, disturbance of soil layers during terracing can also lead to short-term decline in farmland productivity. This study investigates the effects of terracing operations at two sites of different ground substrate configurations in the Loess Hilly Region. Utilizing geochemical and molecular biological analysis methods, we examined the changes in the physicochemical and microbial properties of the ground substrate after terracing, using adjacent sloping farmlands as control sites. The results show that when the ground substrate configuration remained intact, terracing increased the average water content (from 8.44% to 14.34%) and soil organic carbon (from 2.74 g/kg to 5.76 g/kg) by 70% and 110%, respectively, and increased soil microbial α-diversity by 90%. The microbial community structure was also enhanced with an increase in relative abundance of soil- and plant-benefiting genera such as Streptomyces and Nocardioides, thereby promoting plant growth. Conversely, when the ground substrate configuration was altered, terracing led to a decrease in soil nutrient and moisture content, which was detrimental to crop growth. Therefore, maintaining the integrity of the ground substrate configuration is crucial during the terracing process to achieve optimal soil and water conservation outcomes. Full article

Plant growth-promoting rhizobacteria (PGPR) foster sustainable and environmentally friendly agriculture by promoting plant growth and development. PGPR colonize the root rhizosphere, rhizoplane and root tissues, where they drive organic matter turnover and nutrient cycling, thereby increasing the (phyto)availability of essential macro- (P, N, K, S, Ca, Mg) and micronutrients (Fe, Zn, Mn, Mo, Co, Ni, Cu, B). This process relies on various mechanisms, including acid secretion (rhizospheric acidification and metal chelation), siderophore production (binding Fe, Zn, and other metals) and hydrolytic enzyme-mediated catalysis (phosphatases, phytases). Some of these microorganisms can also modulate the phytohormonal balance, reshaping root architecture and enhancing nutrient uptake, and even can alleviate abiotic stress or serve as biocontrol agents, contributing to pathogen resistance. Even though plant cultivation practices relying solely on synthetic fertilizers rapidly increase crop yield and productivity, they eventually result in crops poor in essential micronutrients and trace elements. This may contribute to micronutrient malnutrition in the human population. On the contrary, PGPR enhance both crop yield and nutritional quality. Therefore, in utilization with other nutrient sources, PGPR provide a promising and scalable approach towards advancing environmentally sustainable agriculture systems. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

Optimization of nitrogen (N) management is critical for enhancing maize (Zea mays L.) productivity while maintaining soil health. The present study investigated the impact of split-application fertilization strategies on soil chemical properties and grain yield across three distinct soil types (calcaric fluvisol, luvic phaeozem, and stagnic phaeozem) in Mureș County, Romania, over three cropping seasons (2022–2024). Three fertilization variants were evaluated: the first treatment, designated V1, involved the application of 300 kg/ha NPK 20-20-0 + 300 kg/ha urea, the second treatment V2 utilized 300 kg/ha NPK 20-20-0 + 300 kg/ha NAC 27 N-calcium ammonium nitrate, and the third treatment V3 served as the baseline control, receiving (300 kg/ha NPK 20-20-0). Results indicated that significant differences were observed among the three experimental sites representing contrasting soil types for soil chemical properties and maize productivity. Calcaric fluvisol exhibited the highest production potential, attaining a mean yield of 11,702.78 kg/ha. The impact of N supplementation on soil N levels and maize yield was found to be significant. The variant receiving urea supplementation (V1) achieved the highest median yield of 9560 kg/ha in comparison to the 7420 kg/ha obtained in the control. A strong positive correlation was observed between N index and yield across all soil types (ρ = 0.93 to 0.97, p < 0.001). Fertilization significantly influenced soil pH, CaCO₃ content, nitrogen index, phosphorus availability, and maize yield, whereas humus content remained relatively stable among treatments. These findings indicate that a split-fertilization regime combining NPK with urea provides a favorable balance between productivity and cost-effectiveness and maize output in the Transylvanian Plateau. Full article

Global food production systems are increasingly challenged by population growth, climate change, water scarcity, and environmental degradation, necessitating the adoption of sustainable, resource-efficient food production strategies. Aquaponic systems integrate recirculating aquaculture with hydroponic crop cultivation, enabling nutrient recycling and improved water-use efficiency. Simultaneously, CRISPR/Cas9 genome-editing technology has emerged as a powerful tool for precise genetic improvement of economically important aquaculture traits. This review critically evaluates current progress in CRISPR/Cas9 applications in aquaculture, with emphasis on Nile tilapia (Oreochromis niloticus). Evidence from peer-reviewed studies indicates that targeted modification of genes associated with growth regulation, disease resistance, nutrient metabolism, feed efficiency, and stress tolerance can significantly enhance fish productivity and physiological resilience. Genes involved in hypoxia adaptation and nitrogen metabolism may further improve environmental performance in intensive recirculating systems by reducing ammonia accumulation and enhancing nutrient utilization. However, most genome-editing studies have been conducted under laboratory or conventional aquaculture conditions, with limited information available regarding the long-term performance, ecological interactions, microbial dynamics, and biosafety of genome-edited fish in aquaponic environments. Technical limitations including off-target effects, mosaicism, delivery efficiency, regulatory uncertainty, and public acceptance continue to constrain large-scale implementation. In the short term, CRISPR/Cas9 applications are likely to focus on practical trait enhancement under controlled aquaculture systems, whereas longer-term research may explore fish lines specifically optimized for nutrient cycling, environmental resilience, and integrated aquaponic sustainability. Overall, CRISPR/Cas9-mediated genome editing represents a promising but still emerging strategy for improving sustainable aquaculture and aquaponic food production systems. Full article

Tomato is one of Thailand’s most significant economic crops, generating substantial export value and serving as a primary source of income for local farmers. However, the traditional manual grading process often fails to comply with the Thai Agricultural Standard TACFS 1503–2007, as grading decisions rely heavily on individual experience and subjective perception, resulting in inconsistent quality. Existing automated systems face the challenges of low accuracy, high costs, and complex hardware, while many are incompatible with Thailand’s grading standards. This study presents a multi-view tomato grading system (MVT-Grader), utilizing a dataset acquired from Doi Kham Food Products Co., Ltd. (Third Royal Factory, Tao Ngoi) under controlled lighting conditions. Subsequently, MVT-Grader is built on a custom-designed lightweight CNN architecture with an adjusted spatially aware loss function to enhance the model’s sensitivity in detecting subtle surface defects and color variations. The proposed model was trained using tomato images captured from two and three different viewpoints via a low-cost webcam setup and processed by a GPU-embedded system. Experiments conducted using stratified 5-fold cross-validation on a real-world industrial dataset demonstrate average grading accuracies of 99.43% (two-view) and 99.64% (three-view). Furthermore, the proposed Real-Time Lightweight CNN with Spatially Aware Loss Optimization achieves processing speeds of 87 ms and 114 ms per tomato for two- and three-view cases, respectively. Compared with MVCNN-Siamese, SDF-ConvNets, and Multi-View Spatial Network, the proposed system outperforms the others in both accuracy and speed, improving accuracy by 1.6–6.11% and reducing processing time by 39–49 ms. Full article

Excessive chemical fertilization in maize production has reduced fertilizer-use efficiency and increased pressure on soil quality, whereas reducing fertilizer input without yield loss remains challenging. This challenge has shifted attention toward strategies that improve crop nutrient acquisition and utilization under lower fertilizer supply. Harpin protein-based enzyme complexes may provide a regulatory approach, but their field performance under reduced fertilization remains unclear. A two-year field experiment was conducted from 2023 to 2024 using two maize cultivars, Heyu236 and Fuyuan2. In 2023, the harpin protein-based enzyme complex was applied at 200-fold and 300-fold dilutions under conventional fertilization to identify effective spraying concentrations. In 2024, the same two concentrations were evaluated under conventional fertilization and 15%, 30%, and 45% fertilizer reductions. In the 2023 concentration screening trial under conventional fertilization, the enzyme complex increased kernels per ear by 5.6–9.7% and tended to increase the yield by 0.4–17.2% (not significant). In 2024, under reduced fertilization, enzyme application combined with 30% fertilizer reduction produced a stable yield response. In particular, the 300-fold dilution combined with 30% fertilizer reduction increased kernels per ear by 18.1% and 13.2% and grain yield by 16.9% and 9.5% in Fuyuan2 and Heyu 236, respectively. Soil analyses showed that the enzyme treatment mainly improved nutrient availability, as reflected by higher available P, available K, alkali-hydrolyzable N, organic matter, and available Cu, Zn, Fe, and Mn in the soil. These findings suggest that the harpin protein-based enzyme complex helped maintain maize yield under moderate fertilizer reduction by improving kernel formation and soil nutrient availability. Among the tested treatments, foliar application at 300-fold dilution combined with 30% fertilizer reduction showed the greatest potential for reducing fertilizer input while sustaining maize productivity. Full article

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. Full article

Maize, as a major cereal crop in China, is vital for national food security, and appropriate nitrogen fertilization is essential for its growth and yield. Avoiding excessive nitrogen fertilizer application while maintaining productivity remains a critical challenge for sustainable agriculture. Although straw returning is widely adopted to reduce chemical fertilizer inputs, its effectiveness is often regionally constrained. In the West Liaohe Plain, low temperature and spring drought limit straw decomposition and nutrient release, making it difficult to reduce nitrogen fertilizer input and improve fertilizer use efficiency. Therefore, this study examined the effects of different nitrogen management modes on straw decomposition, nutrient release, mineral fertilizer substitution potential, soil available nutrients, and maize yield under shallow buried drip irrigation with integrated water and fertilizer management. A field experiment was conducted with five nitrogen (N) fertilizer management treatments: a conventional fertilization treatment (CK), in which 15% of total N was applied as starter fertilizer; two increased starter N treatments, in which 30% (30%N) and 45% (45%N) of total N were applied as starter fertilizer; and two organic substitution treatments, in which 30% (30%ON) and 45% (45%ON) of mineral N fertilizer were substituted with decomposed sheep manure based on equivalent total N input. Straw decomposition and nutrient release were measured using the nylon mesh bag method and fitted with an exponential decay model. The mineral fertilizer substitution potential was estimated based on straw nutrient release, while soil available nutrient dynamics in the 0–40 cm soil layer were analyzed, and the Mantel test and PCA were used to assess their relationships. Organic substitution promoted straw decomposition. The 30%ON treatment showed the highest rate at 70.91%, which was 19.2% higher than that of CK, and it exhibited a higher theoretical maximum decomposition rate (a), higher decomposition rate constant (k), and a shorter half-life. All treatments increased nutrient release and soil available nutrients, and organic substitution demonstrated stronger temporal persistence and more uniform vertical distribution among soil layers. The 30%ON treatment increased straw nutrient release by 4.8% to 18.2% and enhanced mineral fertilizer substitution potential. Although the 30%ON treatment did not increase yield in the first experimental year, it showed a significant yield advantage in the second year, which coincided with greater straw nutrient release and higher soil available nutrient levels under this treatment. Substituting 30% of mineral N fertilizer with organic fertilizer under shallow buried drip irrigation (300 kg N ha⁻¹) optimized the C/N balance of the input system and facilitated straw decomposition and nutrient release. The continuous accumulation of soil available nutrients under this treatment, together with sustained straw nutrient release, was associated with a significant yield advantage in the second experimental year. Therefore, the 30%ON treatment may represent an appropriate management strategy for coordinating straw resource utilization, soil fertility maintenance, and stable maize production in the West Liaohe Plain. Full article

Quantifying the nonlinear response of crop systems to meteorological driving factors remains a core challenge in agrometeorology. Although Explainable Artificial Intelligence (XAI) offers new approaches, existing SHAP-based threshold identification methods are largely confined to shifts in effect direction. Furthermore, a unified quantitative grading scale for interaction effects among factors is lacking. To explore the meteorological factor thresholds and interaction effect intensities affecting rice yield, rice unit yield and meteorological data from nine districts and counties in Ningbo City from 1995 to 2024 were utilized. Rice yield prediction models were constructed based on LASSO and six machine learning algorithms. Recursive Feature Elimination (RFE) based on the SHAP algorithm was conducted to screen out 11 core meteorological factors. Building upon this, two innovative methodological indicators were proposed. First, the Derivative Extrema Threshold (DET) was introduced as a supplement to the Zero-Crossing Threshold (ZCT). By locating the extremum points of the first derivative of the smoothed SHAP dependence plot curves, the critical positions where the effect intensity undergoes a qualitative change without a directional reversal were identified. Second, the Interaction Dominance Ratio (IDR) was proposed. This metric normalizes the interaction variability within a total effect framework and establishes a three-tier grading standard for strong, moderate, and weak interactions. It was observed that optimal performance was achieved by the LightGBM model after feature optimization (R² = 0.833). Direction reversal points with extremely narrow confidence intervals, such as an August cumulative precipitation of 210.6 mm and a June average temperature of 24.5 °C, were identified by the ZCT. Intensity mutation characteristics, such as the “weakening of the yield reduction effect” at a May cumulative precipitation of 64.9 mm, were further revealed by the DET. An Interaction Dominance Triangular Network, composed of the August–September average temperature, the June minimum temperature, and the August cumulative precipitation, was accurately characterized by the IDR analysis. This overcomes the constraints of traditional single-factor early warning systems. The “ZCT-DET-IDR” framework constructed in this study facilitates a methodological advancement from directional discrimination and intensity early warning to multi-factor synergistic analysis. This framework provides a quantifiable novel perspective for the refined early warning of regional agrometeorological disasters. Full article

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence. Full article