Search Results (1,988)

Diamondback moth (DBM, Plutella xylostella) outbreaks are shaped by delayed meteorological conditions, yet most forecasting models compress weather into a few monthly summaries and provide limited ecological interpretation. We propose MeteoSCOPE, an ontology-aware sparse Perceiver framework for interpretable, multi-horizon retrospective forecasting of DBM abundance from historical pest records and rich meteorological descriptors. Each feature-lag value is encoded as a token carrying feature identity, ecological group, descriptor type, lag position, and seasonal information; in the rich setting, 138 descriptors across 12 months yield 1656 tokens per sample. Sparse cross-attention compresses these tokens into a compact latent representation, while horizon-specific queries produce one- to four-month-ahead forecasts. Attention tensors and a common-plus-residual branch are aggregated into feature-, group-, descriptor-, lag-, horizon-, and residual-level explanations. Using DBM records from Huiyang and Shantou, Guangdong, MeteoSCOPE achieved the strongest overall retrospective performance, with robust gains at Shantou and metric-dependent gains at Huiyang. The explanations identified pest history as the leading attended group at both sites and surfaced site-specific secondary attributions for soil moisture, weather state, wind, soil temperature, and humidity, treated as model evidence rather than causal ecological effects and corroborated by independent occlusion and KernelSHAP analyses. Strict zero-shot cross-site transfer degrades substantially, so prospective field validation and broader multi-site testing remain required before operational deployment. MeteoSCOPE thus provides a transferable methodological framework (not a deployable forecaster) for interpretable analysis of high-dimensional agricultural time series. Full article

Infectious diseases remain a major global health concern, with a growing burden of antimicrobial resistance and consequent higher mortality in the human population. Accurate bacterial identification is fundamental across clinical, veterinary, agricultural, and research settings, supporting effective diagnosis, antimicrobial stewardship, infection control, food safety, and environmental monitoring; however, conventional approaches are limited by time constraints, reduced sensitivity, and challenges in detecting fastidious or uncultivable organisms. This review provides a comprehensive overview of classical and advanced methods, including microscopy, culture, biochemical testing, immunological and serological assays, proteomic and spectroscopy-based techniques, and molecular approaches, such as polymerase chain reaction (PCR), digital PCR, DNA hybridization, 16S rRNA gene sequencing, whole-genome sequencing, and metagenomics. The integration of artificial intelligence has further enhanced analytical performance. Nevertheless, harmonization of bioinformatics frameworks remains essential, as variability in algorithm-defined cut-off values limits standardized implementation of whole-genome sequencing in routine laboratories. Emerging technologies, including CRISPR-based diagnostics and phage- and nanomaterial-based detection systems, offer promising alternatives. Overall, the integration of these approaches is expected to improve the accuracy, speed, and applicability of bacterial identification across diverse settings; however, these advances should be implemented cautiously, with standardization remaining a key priority alongside technological modernization. Full article

The digital transformation of agriculture is rapidly turning the sector into a highly data-intensive domain. The European Union has responded with a broad horizontal framework encompassing the General Data Protection Regulation (GDPR), the Data Governance Act (DGA), the Data Act, the PSI Directive and the AI Act. However, this framework remains sector-neutral: it does not define ‘agricultural data’ as a legal category, nor does it explicitly recognize the specific position of farmers as data providers. This article pursues three objectives: (i) to map the EU legal and policy framework on data and AI as it applies to agriculture and identify regulatory gaps; (ii) to synthesize key concerns from the literature on agricultural data governance, with particular attention to the position of farmers and data spaces; and (iii) to develop an outline of a Spanish ‘Law on Agricultural Data and Digital Agricultural Services’ as an example of sectoral data legislation. The proposed Act—structured around a Preliminary Title and seven substantive Titles—would define agricultural data, recognize farmers as data providers, establish mandatory contractual protections, govern agricultural data spaces and cooperatives, introduce sector-adapted AI rules, address data sovereignty, and set up an institutional framework and graduated sanctions. The analysis argues that sectoral data law can complement EU horizontal rules, enhance legal certainty, and empower farmers without fragmenting the internal market. The article employs a doctrinal legal analysis and normative design-oriented methodology, drawing on secondary literature, policy documents, and EU and Spanish law; it does not rely on original empirical fieldwork. Full article

Agriculture is currently transitioning toward higher intelligence and facility-based production, where harvesting robots play a crucial role in enhancing efficiency and ensuring standardized output. Addressing the challenges of inaccurate picking pose estimation and limited reachability in greenhouse environments, this paper proposes a reachable grasping pose estimation method based on Particle Swarm Optimization (PSO). First, initial poses are calculated via instance segmentation and keypoint extraction. Subsequently, a fitness function is constructed based on inverse kinematics, and the PSO algorithm is employed to iteratively search for optimal reachable poses. To further tackle planning difficulties in confined spaces, a two-stage path planning method based on cost maps is introduced. A series of performance metrics were designed to validate the proposed pose estimation and path planning methods through simulation experiments. In real-world field tests, the system achieved a harvesting success rate of 85%, significantly outperforming existing methods. The results demonstrate that the proposed approach substantially enhances the operational feasibility and success rate of tomato harvesting robots. Full article

Flowering period management is a critical component of orchard production, significantly influencing the accuracy and timeliness of agricultural decisions such as flower and fruit thinning, yield stabilization, improvement in fruit commodity value, and control of mold core disease. Aiming at the problems of traditional flowering period judgment relying on manual experience, strong subjectivity, low efficiency, and difficulty in large-scale implementation, this study proposes an accurate evaluation model for apple flowering period based on near–far view multimodal visual data. A dedicated near–far view combined vision acquisition system was built to synchronously obtain panoramic images of fruit tree canopies and high-definition close-up images of single flowers/clusters, constructing a multimodal dataset covering the canopy spatial structure and fine floral organ morphology. YOLOv5s and ResNet-50 were employed to extract macro flowering proportion features from far views and micro morphological features from near views, respectively. A feature fusion strategy was introduced to realize the deep fusion of macro–micro features, and finally, a multimodal flowering period classification model was constructed to accurately divide the apple flowering period into four stages: bud stage, initial bloom stage, full bloom stage and late bloom stage. The overall recognition accuracy of the model reached 95.7%. The accurate apple flowering period evaluation system built based on this model has realized the paradigm shift in flowering period judgment from “qualitative manual experience” to “accurate quantification by machine vision”, providing a scientific time window basis for core orchard operations such as pre-flower re-pruning, flowering pollination, fruit setting evaluation and fruit thinning and bagging, and effectively promoting the intelligent and operational development of orchard management. Full article

Artificial Intelligence of Things (AIoT) is transforming Internet of Things (IoT) systems from cloud-centric data processing into distributed intelligence across device, edge, fog, and cloud tiers. However, existing reviews often emphasize specific computational layers, learning paradigms, or application domains rather than the cross-domain problem of Artificial Intelligence (AI) workload placement under real deployment constraints. This paper presents a structured integrative review of AI workload placement in AIoT, based on a multi-stage literature search, two-stage screening process, and thematic synthesis of 132 sources. The review does not propose a new physical architecture; instead, it develops a terminology-harmonized and AI-centric perspective for assessing where AI functions should reside according to latency, privacy, bandwidth, power, scalability, resilience, and model complexity. Evidence is synthesized across Industrial Internet of Things (IIoT), smart cities, Internet of Medical Things (IoMT), and smart agriculture. The findings show that placement drivers are domain-dependent: deterministic response and reliability dominate IIoT, interoperability and scale shape smart cities, privacy and human oversight constrain IoMT, and energy scarcity and intermittent connectivity define agriculture. The review concludes that robust AIoT requires hybrid multi-layer architectures combining Tiny Machine Learning (TinyML), edge/fog coordination, cloud-scale optimization, and Federated Learning (FL) where appropriate. Full article

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture. Full article

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments. Full article

Hilly and mountainous regions are strategically vital for national food security. However, due to complex topographical constraints, their agricultural mechanization levels remain severely underdeveloped. This creates a critical bottleneck in agricultural modernization. Conventional agricultural machinery faces multifaceted challenges in terrain adaptability, operational efficiency, and safety assurance when deployed in these environments, necessitating the urgent development of specialized chassis with enhanced trafficability and stability. Following a systematic literature review of key technologies, including power transmission systems, traveling and support mechanisms, leveling control, and navigation tracking, this study reveals that current chassis technology is advancing toward intelligentization, enhanced efficiency, environmental sustainability, and improved terrain adaptability. The analysis demonstrates that multiple technological pathways, encompassing mechanical, hydraulic, and electric drives, are exhibiting convergent and complementary trends. Future research and development should prioritize the following areas: integrated intelligent coordinated control architectures, green and sustainable power system innovation, modular and reconfigurable platform design, and the establishment of collaborative frameworks among industry, academia, research institutions, and application sectors. Comprehensive standardization systems are also needed. These strategic directions are essential for comprehensively elevating agricultural mechanization levels and maximizing developmental benefits in hilly and mountainous regions. Full article

In response to the challenges of large fruit-scale variation, dense target distribution, severe leaf occlusion, and complex backgrounds in green plum detection within orchards, this paper proposes a lightweight multi-scale edge geometry-aware network (MEGNet). First, the Green Plum Detection Dataset (GPD) is constructed to provide realistic orchard scene data for the task. Next, we enhance the model’s structure based on YOLO11n by designing an efficient multi-scale feature fusion attention module (EMFFA) to improve the expression of multi-scale fruit features. We also introduce a color-edge guided dual-discriminator feature enhancement module (CED) to strengthen feature discrimination in complex backgrounds. A coordinate attention ghost detection head (CAGDetect) is proposed to reduce model parameters and computational complexity. Additionally, a geometry-consistency modulated CIoU loss function (GC-CIoU) is introduced to improve target localization stability in occluded and dense scenes by incorporating a geometric consistency modulation mechanism. Experimental results show that on the GPD, MEGNet achieves a Precision of 93.9%, Recall of 86.2%, mAP₅₀ of 93.2%, and mAP_50:95 of 76.1%. The model’s Parameters are only 2.13 M, with FLOPs of 4.7 G. Compared to the baseline YOLO11n model, Precision, Recall, mAP₅₀, and mAP_50:95 are improved by 2.5%, 5.2%, 4.4%, and 4.6%, respectively. Additionally, deployment experiments on the Jetson Orin Nano embedded device demonstrate real-time detection speeds of 31–33 FPS. The proposed method provides an efficient and reliable solution for intelligent harvesting systems, orchard monitoring platforms, and agricultural robot vision perception. Full article

The utilization of multi-source sensing data to achieve intelligent perception and refined management of farmland has become a vital research direction in modern agriculture. However, traditional inspection approaches based solely on visual information are highly susceptible to illumination variations, occlusion, and background interference, which makes stable pest detection and accurate crop growth assessment difficult to achieve. To address these problems, we propose a multimodal target perception network for intelligent farmland inspection. By integrating UAV imagery, ground environmental sensor data, and spatial location information, joint perception of farmland pests, diseases, and crop growth status is achieved. In the proposed framework, cross-modal alignment and collaborative encoding mechanisms, a multi-scale target perception structure, and a dynamic multimodal fusion strategy are introduced to collaboratively model information within a unified semantic space. Experimental results on a constructed multimodal farmland dataset demonstrate that the proposed method achieved 87.53% Precision and 89.16% mAP in the pest and disease detection task, and 88.04% Accuracy in the crop growth assessment task, significantly outperforming several mainstream visual detection models and multimodal fusion approaches. The results indicate that this intelligent perception framework can significantly improve the robustness of farmland inspection systems, providing an effective technical pathway for AI-driven precision agriculture decision-making. This technology breaks the barrier between production-side sensing data and e-commerce demand, providing a practical technical solution for agricultural production-marketing synergy, quality premium realization and digital rural revitalization. Full article

A scientific understanding of the relationship between artificial intelligence (AI) and agricultural carbon sinks (ACS) is essential for promoting the low-carbon transformation of agriculture in the Yellow River Basin, accelerating the achievement of the “dual carbon” goals, and enhancing the high-quality development of the regional economy. Using panel data from 97 cities in the Yellow River Basin in China from 2001 to 2023, this study measures the levels of ACS and agricultural net carbon sinks (ANCS), and further examines the mechanisms, regional heterogeneity, and moderating effects associated with the impact of AI on both indicators. The results indicate that: (1) AI significantly improves ACS and ANCS in cities within the Yellow River Basin. Specifically, for every 1% increase in AI development, ACS and ANCS increase by approximately 0.0111 million tons and 0.0138 million tons, respectively. This effect is more pronounced in the upper and lower reaches of the basin, while remaining insignificant in the middle reaches; (2) AI promotes improvement in ACS and ANCS by increasing the level of agricultural mechanization and the intensity of chemical fertilizer application; (3) Grain yield per unit area and agricultural planting structure positively moderate the relationship between AI and both ACS and ANCS. Overall, the findings suggest that AI plays a significant positive role in enhancing ACS and ANCS. Therefore, greater emphasis should be placed on the cultivation, diffusion, and application of AI technologies to achieve the sustained improvement of ACS and ANCS capacity in the Yellow River Basin. Full article

In the development of intelligent processing for fresh corn cobs, automated inspection of ear appearance quality to promptly sort out cobs with surface defects and ensure overall product compliance is currently a hot topic in agricultural product processing research. However, fresh corn cob surfaces are covered with numerous independent, densely packed kernels, and defects affecting one or more kernels create surface anomalies of highly variable sizes. This leads to defect targets with multi-scale features and scattered distributions, making it challenging for existing deep learning-based visual inspection methods to simultaneously optimize small-target modeling capacity and computational efficiency. Consequently, these methods cannot effectively balance the accuracy of small-scale defect detection with computational efficiency, making it difficult to meet practical requirements. To address these issues, this paper proposes the SSAD-YOLOv8s-Prune defect detection method for small-scale defect detection in white fresh corn cobs. First, the backbone layer of the original model is replaced with a custom-designed SSA structure, which not only expands the feature dimensions for small-scale defects and enriches feature representation but also reduces the number of computational parameters to achieve model compression. Second, the original neck layer is replaced with a custom-designed RepDyFPN structure to enable feature fusion and interaction across different scales and depths. Finally, the LAMP algorithm is employed to prune and compress the newly improved model, further achieving model compression performance. Compared with the baseline YOLOv8s, our method reduces model parameters by 9.33 M, floating-point operations (FLOPs) by 12.5 G, and model size by 17.6 MB, while simultaneously improving mAP₅₀ by 1.2 percentage points to 96.1% and mAP_50–95 by 4.1 percentage points to 62.8%. Furthermore, our method maintains advantages over other mainstream detection models. Therefore, the proposed SSAD-YOLOv8-Prune detection model successfully balances detection accuracy with model compression, providing a feasible detection method for small-scale defect detection in fresh corn cobs. Full article

In order to better promote the use of intelligent agricultural machinery, enhance the efficiency of grain production, optimize resource utilization, and effectively address the practical problem of the reduction in the rural labor force, while theoretically clarifying the mechanism that affects the adoption of intelligent agricultural machinery by farmers in Changsha County. Based on a questionnaire survey of farmers in Changsha County, Hunan Province, the ordered logit model was used to identify the significant factors influencing farmers’ adoption of intelligent agricultural machinery. The empirical results show that male farmers, farmers with a non-agricultural occupation, and farmers with a lower education level (below high school) have a lower willingness to adopt intelligent agricultural machinery. As the risk of purchasing intelligent agricultural machinery decreases, market demand increases, and the number of agricultural services provided by the government increases, the likelihood of farmers adopting intelligent agricultural machinery also increases. Based on these findings, this paper proposes targeted suggestions aimed at increasing the adoption of intelligent agricultural machinery by farmers in Changsha County, Hunan Province. Full article

As agriculture moves towards green transformation and low-carbon production, the high energy consumption, environmental burden, and residue risks associated with conventional chemical fertilisers, pesticides, and disinfectants have become increasingly prominent. Non-thermal plasma (NTP) can generate reactive oxygen and nitrogen species (RONS) under near-ambient temperature and pressure conditions, while offering low chemical residue, high reactivity, and modular equipment design. It has therefore attracted growing attention in agricultural engineering and green agricultural input preparation. This review focuses primarily on studies published within the past five years, together with the selected foundational literature retrieved from Web of Science, Scopus, PubMed, MDPI, and ScienceDirect. It systematically examines the fundamental mechanisms, application modes, and representative agricultural scenarios of NTP, with particular emphasis on agricultural nitrogen fixation and fertilisation, seed treatment and seedling raising, crop growth regulation and protection, soil improvement and remediation, and postharvest preservation and safety treatment of agricultural products. Key technological advances are then summarised, including optimisation of discharge systems and reactor configurations, plasma–catalysis synergy, preparation of plasma-activated water (PAW) and plasma-activated mist (PAM), and the development and integration of specialised agricultural equipment. In addition, the current state-of-the-art (SOA) of artificial intelligence (AI) applications in plasma-process modelling, process-parameter optimisation, agricultural performance evaluation, and intelligent control is discussed. Existing evidence indicates that NTP is particularly relevant to controlled-environment agriculture, including greenhouse cultivation, hydroponics, and aeroponics, where discharge processes, water or nutrient solutions, and crop root-zone management can be coupled for in situ nitrogen supply, activated-medium preparation, and crop protection. However, reported effects remain strongly dependent on discharge type, energy input, reactive-species composition, treatment dose, crop species, cultivation system, and application route. Therefore, NTP-based agricultural technologies should be evaluated using consistent indicators, including energy consumption, product selectivity, reactive-species stability, treatment throughput, crop response, ecological safety, and system-level integration with AI and IoT. Future research should prioritise high-efficiency reactors, standardised evaluation frameworks, cross-scale mechanistic understanding, reliable datasets, and closed-loop intelligent control, thereby supporting the transition from laboratory studies to reproducible and application-oriented agricultural systems. Full article