[Significance] Digital agriculture is unequivocally the core driving force for modern agricultural transformation, fundamentally aiming to achieve full-process digital mapping and intelligent management of production through the deep integration of advanced information technologies such as the Internet of Things, big data, artificial intelligence (AI), and remote sensing, with earth observation (EO) technology serving as the essential data engine providing indispensable spatial information support for this systemic shift. However, the current landscape of digital agriculture development remains unbalanced, exhibiting a tendency to be "heavy on transactions and light on production", where the core production links suffer from low digitalization penetration rates; furthermore, the profound knowledge embedded within the vast corpus of EO data has yet to be fully extracted and interpreted, leading to a situation where many established algorithms demonstrate insufficient robustness and universality when confronted with the complexity and diversity of global cropping systems, thereby limiting their practical efficacy. Crucially, an over-reliance on technology to optimize production efficiency alone, without ecological guidance, can induce secondary environmental risks, such as exacerbating regional groundwater depletion or contributing to a decline in biodiversity through agricultural landscape simplification, thus necessitating an approach that promotes the deep coupling of EO technology with agronomic principles and local ecological practices to construct a resilient smart agricultural system that achieves a holistic balance between productivity, resource efficiency, and ecological integrity. [Progress] The current research frontiers of EO-driven digital agriculture primarily converge on three critical domains: intelligent crop condition monitoring, digital twin farming systems, and the enhancement of agricultural system resilience. Intelligent monitoring utilizes the fusion of high-resolution remote sensing imagery and machine learning frameworks to enable large-scale, comprehensive crop mapping and the fine-grained identification of crop types at the field scale, with next-generation yield prediction models integrating advanced deep learning techniques to significantly improve accuracy, while remote sensing is also effectively employed for agricultural disaster monitoring. The digital twin farming system represents an advanced stage of precision agriculture, centered on digitally modeling all agricultural production elements to construct a highly consistent virtual replica of the physical environment, operating through a real-time closed-loop mechanism of perception, simulation and analysis, and decision-making support to guide optimal interventions; successful applications include intelligent water resource scheduling in Chinese irrigation districts and the use of AI vision algorithms to manage complex biological processes like crab farming, although the field must overcome the issue of "pseudo-twins" that focuses on mere visualization rather than driving concrete operational decisions. The focus on agricultural system resilience is supported by digital agriculture providing crucial spatial data on global crop yields, cultivated land distribution, and practices like terracing. To illustrate the practical efficacy of these technologies, this paper analyzes two representative application cases. First, the CropWatch system represents a paradigm shift in agricultural monitoring by constructing a "Cloud-Edge" collaborative ecosystem. It integrates machine learning with a "Pre-training, Prompting, and Fine-tuning" large language model (LLM) framework to automate remote sensing-based crop monitoring, report generation and enhance decision-support intelligence. Through open application programming interfaces (APIs) and multi-scale capabilities, CropWatch provides cross-scale information and decision support from macro-level policy support to micro-level farm management, serving as a global public good that bridges the digital divide in developing nations. Second, in the domain of agricultural water management, the ETWatch technical system demonstrates a robust solution for the precise governance of water resources. By achieving high-resolution evapotranspiration (ET) monitoring from basin to field scales, it enables the accurate assessment of water productivity and the optimization of irrigation schedules. Crucially, this technology is successfully embedded into institutional mechanisms, such as water rights allocation and tiered pricing based on actual consumption, thereby realizing a transformation from empirical water use to data-driven, precise regulation. [Conclusions and Prospects] In sum, digital (smart) agriculture is rapidly transcending its role as a mere extension of agricultural informatization to become the "new-quality productivity" driving high-quality agricultural development, achieving this by fundamentally restructuring production factors, enhancing resource efficiency, strengthening risk response capabilities, and promoting value chain upgrading, thereby offering critical momentum for constructing a more efficient, greener, and sustainable modern agricultural system. Given China's pronounced global advantages in the digital economy, information technology, remote sensing, and intelligent equipment, the nation is well-positioned to integrate these strengths to construct comprehensive, full-chain smart agricultural solutions whose mature systemic models and business paradigms can ultimately form a "China Card" in the global agricultural revolution, contributing Chinese wisdom and solutions towards the realization of global food security and the zero-hunger goal.

[Significance] Under climate change, the frequency and intensity of extreme weather events have increased markedly, posing persistent threats to global food security. Agricultural meteorological disasters, including droughts, floods, heat stress, frost damage, and mechanically induced events such as lodging and hail, are increasingly characterized by rapid onset, strong spatial heterogeneity, and compound interactions. Conventional management strategies relying mainly on post-event assessment are insufficient for timely warning and precision intervention. The development of high spatiotemporal resolution remote sensing and integrated observation systems combining satellite, unmanned aerial vehicle (UAV), and ground-based sensing has substantially advanced agricultural disaster monitoring. These technologies enable field-scale characterization of spatial variability and detection of short-duration disaster processes at hourly to daily timescales. This review synthesizes recent progress in sky-air-ground integrated remote sensing for agricultural meteorological disaster management and establishes a unified framework linking monitoring, early warning, and decision-making, with emphasis on hydrological stress, thermal stress, and structural damage. [Progress] At the observation level, a multi-tier sensing architecture has emerged. Satellite remote sensing provides broad coverage and regular revisit cycles, forming the backbone of regional monitoring. Optical sensors support retrieval of crop structural and biochemical parameters, thermal infrared data enable canopy temperature and evapotranspiration estimation, and synthetic aperture radar (SAR) offers all-weather capability for soil moisture and flood detection. Solar-induced chlorophyll fluorescence (SIF) provides direct information on crop photosynthetic function and enables early identification of physiological stress. UAV platforms complement satellites through flexible deployment and centimeter-scale resolution, allowing detailed mapping of canopy temperature and three-dimensional crop structure using multispectral, thermal, and light detection and ranging (LiDAR) sensors. Ground-based meteorological stations and sensor networks provide continuous measurements for calibration and validation, although scaling point observations to spatially continuous products remains challenging. Consequently, multi-sensor integration is evolving from data stacking toward physically complementary constraint frameworks. Methodologically, two dominant approaches of physically based inversion and data-driven recognition are used. Radiative transfer models, surface energy balance methods, and SAR scattering models offer strong physical interpretability but depend on prior information and data quality. Machine learning and deep learning methods effectively capture nonlinear relationships and complex spatial patterns for disaster identification, yet remain limited by interpretability and cross-regional generalization. At the early-warning stage, crop growth models, hydrological models, and spatiotemporal prediction networks are applied to simulate disaster evolution. Hybrid models embedding physical constraints into data-driven frameworks have become a key research direction to enhance predictive robustness. Decision-support systems have expanded from threshold-based rule engines toward optimization algorithms and multi-objective frameworks, enabling warning information to be translated into actionable irrigation scheduling, protective measures, and emergency responses. Regarding specific hazards, drought monitoring has shifted from vegetation indices toward coupling root-zone soil moisture with crop physiological responses, with SIF-based indicators showing strong potential for early stress detection. Flood studies rely primarily on SAR-based inundation mapping and extend toward quantitative damage assessment. Heat and frost stress research emphasizes growth-stage-dependent dynamic thresholds. Lodging monitoring integrates structural parameters derived from optical, LiDAR, and SAR data, while hail-related studies focus on rapid post-event damage mapping. Compound and cascading disasters have become an important research frontier. [Conclusions and Prospects] High spatiotemporal resolution remote sensing has greatly enhanced the observability and early-warning potential of agricultural meteorological disasters. Nevertheless, key challenges remain, including heterogeneous data integration, scale inconsistency, uncertainty propagation, and insufficient coupling among monitoring, warning, and decision-making components. Future progress requires a systems-engineering perspective. Physically guided machine learning can bridge mechanistic understanding and data adaptability, while agricultural disaster digital twins provide a framework for dynamic interaction among observation, simulation, and decision optimization. In parallel, multi-factor time-series risk modeling and multi-agent learning are needed to better represent compound disaster processes and support intelligent, adaptive, and precision-oriented agricultural disaster management systems.

[Objective] Soil quality is crucial for food security, ecosystem health, and sustainable development, but faces degradation due to intensive land use. Accurate soil quality assessment is therefore essential for informed land management and ecological protection. Machine learning has enhanced digital soil mapping (DSM) by improving modeling accuracy through multi-source data integration. Within DSM, soil sampling design is a foundational step that directly influences prediction accuracy, cost, and efficiency. An ideal scheme must balance mapping precision with economic and operational feasibility. This study focuses on soil organic matter (SOM), a core indicator of soil quality affecting fertility, carbon sequestration, and environmental regulation. Precisely mapping its spatial variability is vital for sustainable soil management. To address the need for efficient sampling, the aim of this research is to develop an optimal sampling design method for regional-scale SOM mapping, reduce sampling redundancy and cost while improving spatial prediction accuracy. [Methods] A sampling optimization framework was proposed that integrated intelligent optimization algorithms with a hybrid spatial interpolation model. The framework was built upon the hippopotamus optimization algorithm (HO) and incorporated the random forest residual kriging (RFRK) method to construct an optimal sampling strategy for the spatial prediction of SOM. At the initialization stage, a population of candidate solutions, referred to as "hippopotamuses", was randomly generated, with each individual representing a potential sampling layout. The HO was employed to select subsets of sampling points from the training sample pool, with each subset forming a candidate solution. Collectively, these solutions constituted the initial hippopotamus population. The study area was located in Lanxi city, Zhejiang province, where a total of 1 080 field-measured soil samples were collected. These samples were partitioned into a training set (n=756), a validation set (n=108), and a test set (n=216) at a ratio of 7:1:2. Environmental covariates, including terrain attributes, vegetation indices, and climate factors, were extracted from multi-source remote sensing datasets. Using these covariates, the HO optimized sampling schemes across varying densities and spatial configurations. The resulting designs were then evaluated using the RFRK model to assess their SOM prediction performance. This process enabled the identification of the optimal sampling density and spatial layout that balanced accuracy and cost-efficiency. [Results and Discussions] When the HO-RFRK framework was applied, the prediction accuracy of SOM improved significantly as sampling density increased from 0.5 to 2.3 points/km² (136-629 points). The root mean square error (RMSE) on the test set decreased from 6.04 to 5.11 g/kg, representing a reduction of approximately 15.4%. The lowest prediction errors were observed at a sampling density of 2.3 points/km², with the RMSE and mean absolute error (MAE) reaching their minimum values of 5.11 and 3.79 g/kg, respectively, beyond which further increases yielded only marginal gains, indicating diminishing returns. To assess the effectiveness of HO, its performance was compared with three established methods: conditioned Latin hypercube sampling (cLHS), genetic algorithm (GA), and particle swarm optimization (PSO). At lower densities (0.5－1.3 points/km²), all methods showed limited predictive power. However, at 1.4 points/km² (383 points), the HO method was the first to exceed predefined accuracy thresholds (coefficient of determination, R²>0.40; Lin's concordance correlation coefficient, LCCC>0.55), achieving R²=0.41 and LCCC=0.57, outperforming cLHS (R²=0.38, LCCC=0.53), GA (R²=0.39, LCCC=0.52), and PSO (R²=0.38, LCCC=0.51). Across the range of 1.4－2.3 points/km², HO consistently delivered superior results. At 2.3 points/km², the HO-RFRK combination achieved R²=0.49 and LCCC=0.63, surpassing cLHS, GA, and PSO in both metrics. [Conclusions] Based on the cultivated land of Lanxi city as a test case, a novel sampling optimization strategy was proposed based on the HO. First, the strategy successfully identified an optimal sampling density that maximizes prediction accuracy, as well as a lower, cost-effective density that maintains robust predictive performance with substantially reduced survey costs, defining a practical density range that balances precision and economic feasibility. Second, the RFRK model consistently demonstrated superior prediction accuracy compared to the standard random forest (RF) model across all tested sampling schemes, validating the effectiveness of the integrated HO-RFRK approach. In summary, this optimized strategy achieves high mapping accuracy with greater sampling efficiency, offering a scientifically grounded and practical methodology for reducing long-term soil monitoring costs. It provides a valuable reference for optimizing soil surveys in Lanxi city and other regions with similar environmental settings.

[Objective] Detecting dense and small aquaculture net cages in complex backgrounds is difficult, the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management. [Methods] A dataset of aquaculture net cages was constructed using high-resolution remote sensing imagery collected from seven representative farming regions (Australia, Canada, Chile, Croatia, Greece, China, and the Faroe Islands), and Cage-YOLO, a deep learning model based on YOLOv5, was proposed for detecting dense and small aquaculture net cages. First, an adaptive dense perception algorithm was introduced, which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages. Second, an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities. Finally, a mixed attention block was incorporated to further enhance the model's perception of dense and small objects. [Results and Discussions] Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision, recall, and mean average precision by 5.6, 21.8, and 17.4 percentage points, respectively. The model size was maintained at 16.9 MB, demonstrating both strong performance and deployment advantages. [Conclusions] This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture.

[Objective] As a major crop in protected horticulture, cluster tomatoes grow in clusters with dense overlapping fruits. In greenhouse environments, light conditions are complex and variable, and the fruit color transitions continuously from green to red across different ripening stages, showing continuous gradation characteristics. These factors result in the low efficiency and strong subjectivity of traditional manual recognition methods. Meanwhile, deep learning-based detection models often suffer from decreased detection accuracy, large localization errors, and slow inference speed when facing complex backgrounds and color interference, making it difficult to meet the dual requirements of real-time performance and high precision in practical applications. Therefore, to meet the practical application requirements of high accuracy, high real-time performance, and strong robustness for cluster tomato ripeness detection, this paper proposes a lightweight target detection model for cluster tomato ripeness, namely LampCT-YOLO (Cluster Tomato YOLO with LAMP pruning), which is based on improved YOLOv10. Through structural optimization and lightweight transformation of the baseline model, the detection accuracy, inference speed, and robustness are effectively improved, providing a novel technical solution for cluster tomato ripeness detection. [Methods] Taking YOLOv10 as the baseline model, first, the issue of insufficient feature extraction capability in complex scenarios was addressed by introducing the SegNeXt attention mechanism into the backbone network. By adaptively adjusting attention weights and calculating the correlation matrix between different feature channels, the mechanism automatically identified color channels strongly associated with the three ripeness levels of cluster tomatoes and assigned them higher attention weights, while suppressing feature responses from irrelevant background channels such as greenhouse frames, soil, and irrigation pipes. To achieve lightweight deployment of the model and meet the real-time detection requirements of edge devices, a gradient-based global channel importance method—LAMP channel pruning technology—was introduced after model training. The core principle of this technology was to evaluate the contribution of each channel to the model's detection performance by calculating the gradient magnitude of channels in each network layer, thereby eliminating redundant channels. This significantly reduced the model size and computational complexity while effectively maintaining the model's high detection performance for the three-category ripeness classification of cluster tomatoes. [Results and Discussions] Experiments showed that under the environment of NVIDIA A100 graphics card, for 240 cluster tomato images in the test set, the LampCT-YOLO model exhibited excellent detection performance. The mean average precision at 50 intersection over union (mAP50) for the early ripe, mid-ripe, and late ripe stages of cluster tomatoes was 84.6%, 89.5%, and 88.4%, respectively, which represented increases of 5.5, 7.7, and 0.9 percentage points compared with YOLOv10. The average mAP50 for the three ripeness categories of cluster tomatoes reached 87.6%, a 4.7 percentage points improvement over YOLOv10, demonstrating outstanding performance in both detection accuracy and stability. In addition, the model was found to maintain high recognition accuracy when facing variations in light intensity, fruit occlusion ratio, and background complexity, indicating good robustness and environmental adaptability. Regarding the lightweight effect, after applying the LAMP channel pruning technology, the number of model parameters and computational complexity were reduced by 63.07% and 50.06%, respectively, while the inference speed was improved by 23.1%. This effectively met the requirements of edge computing devices for real-time detection and low power consumption, alleviating the trade-off between model accuracy and inference speed. To verify the practical application value of the LampCT-YOLO model, the model was deployed on a self-developed fruit and vegetable inspection robot, which conducted field tests on 456 clusters of tomatoes in a real greenhouse environment. The results showed that the inspection robot successfully identified 78, 61, and 248 clusters of early ripe, mid-ripe, and late ripe cluster tomatoes, respectively, with detection accuracies of 84.8%, 87.1%, and 84.4%, and an average accuracy of 85.4%. Meanwhile, there were 5, 7, and 10 false detections, as well as 9, 2, and 36 missed detections for the early ripe, mid-ripe, and late ripe stages respectively, which to a certain extent reflected the practical application potential of the model. [Conclusions] The optimized LampCT-YOLO model not only significantly improves the recognition accuracy of cluster tomatoes at different ripening stages but also greatly reduces the model complexity, successfully achieving efficient deployment in resource-constrained scenarios. This model effectively balances the dual requirements of detection accuracy and real-time performance for inspection robots, and further constructs a reusable technical framework for the ripeness detection of protected horticultural fruits and vegetables. It provides strong support for the transformation of protected agriculture from labor-intensive to technology-intensive, and injects key innovative impetus into the large-scale and diversified implementation of smart agriculture.

[Objective] Pears are a common fruit rich in vitamins and minerals. Traditional pear grading primarily relies on manual inspection, which is not only laborious but also susceptible to subjective factors, leading to unstable and inaccurate results. Furthermore, manual operations may cause varying degrees of physical damage to pears, affecting their appearance and market value. Therefore, developing an automated, efficient, and reliable pear grading technology has become an urgent demand in the industry. To address the current problem of poor detection accuracy caused by the small scale of surface defects in Dangshan pears, a lightweight high-precision model was proposed based on an improved Mamba-YOLO architecture, aiming to balance detection accuracy and efficiency. [Methods] The dataset comprised 1 000 images, which were partitioned into training, validation, and test sets in an 8:1:1 ratio. The following improvements were made to the network architecture. Firstly, a dynamic upsampling (Dysample) module was adopted. Compared to the existing upsampling module in Mamba-YOLO, the Dysample module featured fewer parameters and floating-point operations (FLOPs). Its design eliminated complex dynamic convolution kernels, requiring only a small number of linear layers and grouping operations, thereby preserving computational efficiency while enhancing the retention of defect details. Secondly, regarding pear surface defect detection, defects often exhibited high-frequency local features, whereas traditional convolutional neural networks (CNNs) suffer from insufficient feature capture and imbalanced frequency response. As the dilation rate increased, the frequency response of the convolution kernel decreased and its bandwidth narrowed, consequently limiting its ability to process high-frequency information. Therefore, a frequency-adaptive dilated convolution (FADC) module was proposed, which dynamically adjusted the convolution kernel size, enabling the network to adaptively select matching kernels based on local input features. Smaller kernels were used in high-frequency regions, and larger kernels in low-frequency regions, thereby achieving collaborative optimization of multi-band features and enhancing the ability to extract defect features. Finally, considering that using only single-scale depthwise convolutions to capture local features might lead to insufficient perception of input feature information, and that traditional gating mechanisms may lack adequate global context information modeling, the squeeze-and-excitation module was fused with a channel mixer based on the convolutional gated linear unit (CGLU). This combination was extended into a multi-scale version termed MS-CGLU. By incorporating convolutional kernels of different sizes to extract multi-scale features, followed by weighted fusion, stronger feature representation was achieved. [Results and Discussions] The proposed method was rigorously evaluated on the dangshan pear test set. Ablation experiments demonstrated that introducing the CGLU, FADC, and Dysample enhanced detection performance, confirming the effectiveness of these modules. Compared to YOLOv8n, Gold-YOLO-N, and YOLOv12n, the mean average precision (mAP) was higher by 4.7, 5.3, and 6.3 percentage points, respectively. Compared to the baseline Mamba-YOLO-T, the mAP increased by 3.4 percentage points and the frames per second improved by 10.8 percentage points. Furthermore, in comparative experiments with larger-scale models from the same Mamba-YOLO series, the proposed algorithm still demonstrated significant advantages, i.e., its parameter count was only 41.7% of Mamba-YOLO-B and 15.7% of Mamba-YOLO-L, and its FLOPs was merely 57.1% and 18.1% of the respective models, yet it achieved increases in mAP@0.5 of 3.2% and 1.4%, and increases in mAP@0.5:0.95 of 3.1% and 2.6%, respectively. [Conclusions] This research developed a high-precision and lightweight algorithm for detecting surface defects on Dangshan pears. It achieved a superior balance between detection accuracy and inference speed, significantly outperforming relevant lightweight benchmarks and even larger models within its own family in terms of efficiency. This work can provide reliable algorithmic support for lightweight detection research of pear surface defects.

[Objective] The precise quantification of rice seeds within individual cavities of seedling trays constitutes a critical operational parameter for optimizing seeding efficiency and fine-tuning the performance of air-vibration precision seeders. Achieving high accuracy in this task directly impacts resource utilization, seedling uniformity, and ultimately crop yield. However, the operational environment presents significant challenges, including complex backgrounds, seed overlap, variations in lighting and seed orientation, and the inherent difficulty of distinguishing individual seeds within dense clusters. These factors often lead to suboptimal performance in existing automated detection systems, manifesting as low detection accuracy and an inability to achieve robust, precise instance segmentation of individual rice seeds. To address these persistent limitations and advance the state-of-the-art in precision seeding monitoring, an integrated framework for rice seed instance segmentation was proposed. The core innovation lies in the synergistic combination of a cross-modal grounding generation (CGG) network with a pretrained model, which is designed to leverage complementary information from visual and textual domains. [Methods] The proposed methodology fundamentally aimed to bridge the gap between visual perception and semantic understanding within the specific context of rice seed detection. The CGG-pretrained model framework achieved this through deep joint alignment of visual features extracted from seedling tray images and textual features derived from contextual knowledge. This cross-modal grounding enabled collaborative learning, where the visual processing stream (handling object localization and pixel-level segmentation) was continuously informed and refined by the semantic understanding stream (interpreting context and relationships). Specifically, the visual backbone network processes input imagery to generate feature maps, while the pretrained language model component, which utilized contextual embeddings, generated semantically rich textual representations. The CGG module acted as the fusion engine, establishing explicit correspondences between specific regions in the image (potential seeds or clusters) and relevant semantic concepts or descriptors provided by the pretrained model. This bidirectional interaction significantly enhanced the model's ability to disambiguate overlapping seeds, resolved occlusions, and accurately delineated individual seed boundaries under challenging conditions. Key technical innovations validated through rigorous ablation studies include: (1) The strategic use of the bootstrapping language-image pre-training (BLIP) model for generating high-quality pseudo-labels from unlabeled or weakly labeled image data, facilitating more effective semi-supervised learning and reducing annotation burden, and (2) the application of bidirectional encoder representations from transformers (BERT)-based word embed to capture deep semantic relationships and contextual nuances within textual descriptors related to seeds and seeding environments. [Results and Discussions] The ablation experiments demonstrated a pronounced synergistic effect when the core improvements were combined, resulting in a segmentation accuracy improvement exceeding 3 percentage points compared to the baseline model that lacking the integration. Comprehensive experimental evaluation demonstrated the superior performance of the proposed CGG model against established benchmarks. Under the standard intersection over union (IoU) threshold of 0.5, the model achieved a mean average precision (mAP) of 90.7% for bounding box detection (denoted as mAP50^bb for detection) and an outstanding 91.4% mAP for instance segmentation (denoted as mAP50^seg for segmentation). These results represented a statistically significant improvement over leading contemporary models, including region-based convolutional neural network (Mask R-CNN) and Mask2Former, which highlighted the efficacy of the cross-modal grounding approach in accurately localizing and segmenting individual rice seeds. Further validation within realistic seeding trial scenarios, which involved direct comparison with meticulous manual annotations, confirmed the model's practical robustness. The CGG model attained the highest accuracy in two critical operational metrics: (1) Precision in segmenting individual seed instances (single-seed segmentation accuracy), and (2) accuracy in determining the exact seed count per cavity, and it achieved an average accuracy of 88% for per-cavity quantification. Moreover, the model exhibited superior performance in minimizing estimation errors for cavity seed counts, as evidenced by its significantly lower error metrics: a root mean square error (RMSE) of 16.8 seeds, a mean absolute error (MAE) of 13.7 seeds, and a mean absolute percentage error (MAPE) of 2.46%. These error values were markedly lower than those recorded by the comparison models, which underscored the CGG model's enhanced reliability in practical counting tasks. The discussion contextualized these results and attributed the performance gains to the model's ability to leverage semantic context to resolve ambiguities inherent in visual-only approaches, particularly in dense and overlapping seed scenarios common in precision seeding trays. [Conclusions] The developed CGG-pretrained model integration presents a significant advancement in automated monitoring for precision rice seeding. The model successfully addresses the core challenges of low detection accuracy and imprecise instance segmentation for seeds in complex environments. Its high accuracy in both individual seed segmentation and per-cavity seed count quantification, coupled with low error rates, demonstrates strong potential for practical deployment. Importantly, the model enables real-time detection of rice seeds during the image analysis stage, this functionality provides a quantifiable, data-driven basis for making immediate operational decisions, most notably enabling the targeted precision reseeding of empty or under-seeded cavities identified during the seeding process. By ensuring optimal seed placement and density from the outset, the technology contributes directly to improved resource efficiency (reducing seed waste), enhanced seedling uniformity, and potentially higher crop yields. Future work will focus on further optimizing inference speed for higher-throughput seeding lines and exploring generalization to other crop types and seeding mechanisms.

[Objective] In field environments under natural conditions, leaf occlusion and mutual plant shading pose significant challenges to the accurate identification of carrot seedlings. Furthermore, practical agricultural applications often rely on edge devices with limited computational power, necessitating a detection model that combines lightweight design, high accuracy, and robust anti-occlusion capability. The purpose of this research is to develop a robust recognition method for carrot seedlings suitable for complex field conditions, thereby enhancing the accuracy and efficiency of seedling emergence statistics in automated seedling raising processes and providing reliable technical support for precise farm management. [Methods] The CD-YOLO (Carrot Detection-You Only Look Once), a lightweight detection model was proposed based on an improved YOLOv11s. First, to reduce model complexity, several standard convolutions in the backbone network were replaced with depthwise separable convolutions (DWConv), thereby decreasing floating-point operations (FLOPs) and the number of parameters, establishing a lightweight foundation for edge deployment. Secondly, the efficient multi scale attention (EMA) mechanism was embedded into the critical feature extraction module C3k2, constructing a C3k2_EMA module. This module enhanced dynamic perception of local key features and reconstructed cross-scale contextual dependencies broken by occlusion through its parallel multi-branch structure, effectively suppressing background and occlusion noise. Finally, the DynamicHead detection head was introduced. Leveraging its scale-aware and spatial-aware mechanisms, it achieved a dynamic fusion of multi-level features and adaptive weight adjustment, further improving the model's decision-making robustness in complex scenes. To comprehensively evaluate model performance, a carrot seedling dataset covering various field scenarios was independently constructed. Through offline data augmentation, the original 1 274 images were expanded to 4 796, which were then split into training, validation, and test sets in an 8:1:1 ratio. Meanwhile, to systematically quantify the model's anti-occlusion performance, an occlusion severity assessment criterion based on the overlapping area of bounding boxes was proposed. Targets were categorized into three occlusion levels: mild, moderate, and severe. Based on this, a dedicated "Occlusion Test Subset" was separated from the main test set, providing an objective and reproducible benchmark for evaluating the model's anti-occlusion capability. [Results and Discussions] Experimental results on the custom dataset demonstrated that CD-YOLO comprehensively improved detection performance while maintaining its lightweight characteristics. Compared to the baseline model YOLOv11s, CD-YOLO reduced computational load by 6.2 GFLOPs (a 28.8% decrease), decreased model size by 4.8 MB (a 25.0% reduction), improved single-image inference speed by 4.7 ms, reaching 9.6 ms. Concurrently, precision, recall, and mean average precision (mAP_0.5) increased by 3.0, 1.5, and 2.4 percentage points, respectively, ultimately reaching 81.2%, 76.4%, and 84.0%. In comparisons with other lightweight backbone networks like MobileNetv3 and ShuffleNetv2, CD-YOLO consistently outperformed them on the accuracy-speed comprehensive metric, validating the effectiveness of its improvement strategies. In occlusion performance tests, the missed detection rate of CD-YOLO on the occlusion test subset was 13.4%, a 5.7 percentage points decrease compared to YOLOv11s. Its mAP_0.5 on the occlusion subset reached 80.6%, a 5.1 percentage points improvement over the baseline, whereas the improvement on the regular subset was 1.8 percentage points, proving the model's enhanced efficacy in occlusion scenarios. After deploying the model on an NVIDIA Jetson Orin NX edge device and accelerating it with TensorRT, the inference frame rate increased to 32.5 f/s. On random test images, CD-YOLO achieved missed detection and false detection rates of 5.1% and 2.7%, respectively, representing decreases of 7.7% and 2.6% compared to YOLOv11s, demonstrating promising practical application potential. Ablation studies and feature map visualizations further indicated that DWConv, C3k2_EMA, and DynamicHead formed a synergistic optimization loop: DWConv achieved computational compression, freeing up computational budget for subsequent modules; C3k2_EMA enhanced local perception and contextual reconstruction of occluded targets during the feature extraction stage; and DynamicHead performed dynamic fusion of multi-scale features at the decision-making end. Together, they ensured high-precision detection of incomplete targets under limited computational resources. [Conclusions] Through the synergistic design of "lightweighting, feature enhancement, and dynamic fusion", the CD-YOLO model achieved an excellent balance between computational efficiency, detection accuracy, and anti-occlusion capability. The model not only significantly reduced reliance on the computational power of edge devices but also effectively improved robustness and adaptability in complex field environments through structured attention and dynamic fusion mechanisms.

[Objective] The first flowering date of apples is a key phenological stage in the annual growth cycle of fruit trees. Its occurrence timing is directly associated with pollination efficiency, fruit set rate, and subsequent fruit development, and it also serves as an important basis for orchard management practices, including flower and fruit thinning, pest and disease control, as well as early risk warning and emergency management for low-temperature frost events during the flowering period. Existing studies still have room for improvement in the fine-scale extraction of temperature time-series information and in the representation of model adaptability across different spatial locations. Therefore, the purpose of this research is to develop a prediction method for the first flowering date of apples that can effectively characterize time-varying temperature patterns and achieve regional adaptability, thereby providing more reliable technical support for refined orchard management and disaster prevention. [Methods] A deep learning-based forecasting framework for predicting the first flowering date of apples was developed based on observation sites in Luochuan county, Shaanxi province. First, daily near-surface air temperature (NSAT) data from 2019 to 2021 were collected for the period from apple harvest to the subsequent flowering season in the study area, including daily maximum, mean, and minimum temperatures. In addition, elevation, latitude, and longitude were introduced as static geographic factors, forming a combined input composed of dynamic temperature sequences and static spatial attributes. Second, in terms of the model design, a bidirectional long short-term memory network (Bi-LSTM) was employed as the temporal encoder to learn bidirectional dependencies within the temperature time series. On this basis, a customized multi-head attention (MHA) mechanism was integrated, consisting of a local dependency head, a global trend head, and a cumulative feature head, which were designed to represent short-term pre-flowering temperature fluctuations, overall temperature trends, and cumulative temperature effects, respectively. This configuration enhanced the extraction of time-varying information across multiple temporal scales. The attention outputs were then fused with the static geographic factors, and the predicted first flowering date was generated through a regression layer, enabling regionally adaptive prediction. To ensure comparability of results, LSTM and Bi-LSTM models were simultaneously constructed as baseline models using identical data preprocessing and training procedures.Third, Bayesian optimization was applied for automatic hyperparameter tuning, during which key parameters, including learning rate, number of network layers, number of hidden units, regularization terms, and optimizers, were systematically searched, and the optimal configuration was selected based on validation performance. Finally, a cross-year validation strategy was adopted to evaluate model generalization ability: Data from 2019 to 2021 were used as the modeling dataset (training and validation), while the observed first flowering date in 2022 served as an independent test dataset. The predictive performance of all models was evaluated using three widely recognized metrics: root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R). [Results and Discussions] The proposed model achieved an RMSE of 1.34 d, a MAE of 1.13 d, and the R of 0.84 on the test dataset, with most prediction errors concentrated within a range of 0－2 d. Validation results indicated that the proposed approach was capable of providing stable predictions approximately 15－20 d in advance within the study area. Further comparative analysis demonstrated that the Bi-LSTM architecture more effectively exploited both forward and backward dependencies in the pre-flowering temperature time series, thereby offering a more stable temporal representation for regression-based prediction of the first flowering date. Building upon this structure, the introduction of three attention heads: the local dependency head, the global trend head, and the cumulative feature head, enabled the model to more explicitly distinguish and utilize short-term fluctuations, stage-wise trends, and cumulative temperature effects. This targeted extraction of multi-scale time-varying information contributed to reduced prediction errors and improved overall prediction accuracy. Ablation experiments involving static geographic factors further verified the necessity of the spatial adaptability component. When the elevation was removed, the RMSE increased from 1.34 d to 1.45 d. Removing latitude and longitude led to a larger increase in RMSE to 2.54 d, and when both elevation and geographic coordinates were excluded, the RMSE further rose to 2.69 d accompanied by a decrease in correlation. These results indicated that geographic factors provided effective spatial constraints, which supported the learning of location-specific phenological responses across different sampling sites. In addition, spatial prediction maps revealed that the first flowering date in the study area exhibited a gradient distribution with respect to elevation to a certain extent. This spatial pattern was consistent with the modeling rationale of incorporating geographic factors into a unified prediction framework. [Conclusions] This study proposes a deep learning-based prediction method for the first flowering date of apples that integrates multi-dimensional temperature features, a multi-head attention mechanism, and geographic factors. The proposed method achieves relatively high prediction accuracy in cross-year forecasting and enables spatially adaptive prediction of the first flowering date of apples. These findings provide a new data-driven technical pathway for refined prediction of apple flowering phenology and offer important technical support for orchard flowering management, frost damage prevention, and agricultural production decision-making.

[Objective] Maize is one of the most important staple crops in the world and serves as a cornerstone of food security and agricultural sustainability. Accurate and timely prediction of maize yield is essential for optimizing agricultural management practices, supporting market regulation, and guiding policy decisions related to food supply and climate adaptation. In recent years, data-driven yield prediction methods based on machine learning and deep learning have achieved notable improvements in predictive accuracy. However, most existing approaches primarily rely on statistical correlations among variables and often treat influencing factors as independent predictors, without explicitly addressing the complex causal mechanisms and time-lagged interactions that govern crop growth processes. This limitation may lead to reduced model interpretability and compromised robustness under changing environmental conditions. To address these challenges, a novel maize yield prediction framework that integrates causal inference with a hybrid deep learning model was proposed, aiming to improve both predictive performance and mechanistic understanding. [Methods] Multi-source heterogeneous datasets collected across the maize growing season were utilized, including remote sensing-derived vegetation indices, meteorological variables (such as temperature and precipitation), soil profile moisture measurements at multiple depths, and crop observation data corresponding to key phenological stages. First, the Peter-Clark and momentary conditional independence (PCMCI) causal discovery algorithm was applied to systematically identify causal relationships between maize yield and its potential driving factors. The PCMCI method enables the detection of both contemporaneous and time-lagged causal links while effectively controlling for confounding effects in high-dimensional time series data. Through this process, the causal structure of yield formation was explicitly characterized, and key variables with statistically significant causal impacts were selected as inputs for the prediction model. Subsequently, a hybrid moving average, convolutional neural network-long short-term memory (MA-CNN-LSTM) model was constructed to capture the complex spatiotemporal patterns in the causally screened input variables. Specifically, a moving average module was employed as a preprocessing step to suppress high-frequency noise and enhance signal stability. A CNN was then used to extract latent correlation features among multiple variables, reflecting their joint influence on yield formation. Finally, an LSTM network was adopted to model temporal dependencies and cumulative effects across the growing season, enabling effective representation of dynamic yield responses. [Results and Discussions] The causal analysis revealed that soil moisture at depths of 10 cm and 50 cm exerted a significant positive influence on maize yield (P < 0.01), with deeper soil moisture showing a stronger and more persistent time-lagged effect. This finding highlighted the critical role of subsurface water availability in sustaining crop growth during later developmental stages. In addition, vegetation indiced such as the modified chlorophyll absorption ratio index and the normalized difference vegetation index exhibited significant short-term causal relationships with yield during the mid-growth stage of maize, indicating their sensitivity to canopy structure and photosynthetic activity during this period. Comparative experiments conducted against traditional statistical models and conventional machine learning approaches demonstrated that the proposed PCMCI-MA-CNN-LSTM framework consistently achieved superior predictive performance. On the test dataset, the coefficient of determination (R²) reached 0.955, while the mean absolute error (MAE) and root mean square error (RMSE) were reduced to 1.201 kg/mu and 1.474 kg/mu (1 hm²=15 mu). These results indicated that incorporating causal variable selection effectively enhances model accuracy and stability by reducing redundant and spurious correlations. [Conclusions] The results confirm that incorporating causal analysis into yield modeling provides a robust basis for identifying key driving variables and effectively enhances the accuracy and interpretability of maize yield prediction. The proposed framework offers a promising approach for precision agriculture and decision support in crop yield forecasting, particularly under complex and dynamic agro-environmental conditions.