[Objective] When deep learning is applied to plant disease recognition tasks, model fine-tuning faces significant challenges, including limited computational resources and high parameter update overhead. Although traditional low-rank adaptation (LoRA) methods effectively reduce parameter overhead, their strategy of assigning a uniform, fixed rank to all layers often overlooks the varying importance of different layers. This approach may still lead to constrained optimization in critical layers or resource waste in less significant ones. To address this limitation, a dynamic rank allocation (DRA) algorithm is proposed in this research. The DRA algorithm is designed to evaluate and adjust the required parameter resources for each layer during training, enhance the accuracy of plant disease classification models while more efficiently balancing computational resources. [Methods] Public datasets of the Wheat Plant Diseases Dataset and the Plants Disease Dataset were utilized in the experiments. The Wheat Plant Diseases Dataset comprised 13 104 images covering 15 types of wheat diseases such as black rust and fusarium head blight, while the Plants Disease Dataset included 37 505 images of 26 types of plant diseases such as algal leaf spot, corn rust, and bacterial spot of tomato. These datasets were captured under varied lighting, different backgrounds, diverse angles, and at various stages of plant growth. A cross-layer feature similarity metric based on centred kernel alignment (CKA) was introduced to quantify the representational correlation between different layers. Concurrently, a correction factor was constructed based on gradient information and activation intensity to measure the direct impact of each layer on the loss function. These two metrics were then fused using a weighted harmonic mean to generate a comprehensive importance score, which was subsequently used for the initial rank allocation. Furthermore, considering the effect of feature representation changes during training, a stability-triggered adaptive rank update strategy rank re-allocation (RRA) was proposed. This strategy monitored the average parameter change of the low-rank adapters during the training process to determine the convergence state. When this change fell below a specific threshold, the low-rank matrices were merged into the original weights, and the rank allocation table was then re-calculated and updated. This process ensured that more resources were allocated to critical layers, thereby achieving an optimized allocation of parameter resources across different layers. [Results and Discussions] Tests on four models (AlexNet, MobileNetV2, RegNetY, and ConvNeXt) indicated that, compared to full-parameter fine-tuning, the proposed method reduced resource consumption to 0.42%, 2.46%, 3.56%, and 1.25%, respectively, while maintaining a comparable average accuracy. The RRA strategy demonstrated continuous parameter optimization throughout the model's training. On the ConvNeXt model, the trainable parameters on the plants disease dataset were progressively reduced from 18.34 to 9.26 M, a reduction of nearly 50%. In comparison with the standard LoRA method (R=16), the method reduced the accuracy by 0.38, 0.40 and 0.05 percentage points on the wheat plant diseases dataset for AlexNet, MobileNetV2, and RegNetY, respectively, while resource consumption was reduced by 59.3%, 87.4% and 50.5%. Robustness was tested by applying perturbations to the test set, including Gaussian noise, random cropping, color jitter, and random rotation. The results showed that the model was most affected by color jitter and random rotation on the Plants Disease Dataset, with accuracy decreasing by 6.02 and 5.11 percentage points, respectively. On the wheat plant diseases dataset, the model was more sensitive to random cropping and random rotation, with accuracy decreasing by 4.33 and 4.40 percentage points, respectively; the overall performance degradation remained within an acceptable range. When compared to other advanced low-rank methods such as AdaLoRA and DyLoRA under the same parameter budget, the DRA method exhibited higher accuracy. On the RegNetY model, the DRA method achieved an accuracy of 90.96% on the Plants Disease Dataset, which was 0.55 percentage points higher than AdaLoRA and 0.94 percentage points higher than DyLoRA. In terms of training efficiency on the Plants Disease Dataset, the DRA method required 43.5 minutes to reach its peak validation accuracy of 89.84%, whereas AdaLoRA required 52.3 minutes, representing a training time increase of approximately 20.23%. Regarding inference flexibility, the DyLoRA method was designed to generate a universal model capable of adapting to multiple rank configurations after a single training run, allowing for dynamic rank switching during inference based on hardware or latency requirements. The DRA method, however, did not possess this inference-time flexibility. It was focused on converging to a single, high-performance rank configuration for a specific task during the training phase. [Conclusions] The low-rank adaptive fine-tuning method proposed in this research significantly reduced the number of model training parameters while ensuring plant disease recognition accuracy. Compared to traditional fixed-rank LoRA and other advanced low-rank optimization methods, it demonstrated distinct advantages, providing an effective pathway for efficient model deployment on resource-constrained devices.

[Objective] Extracting valuable knowledge from vast amounts of dispersed, heterogeneous, and unstructured agricultural big data, correlating and structuring it, and enhancing large models to form intelligent question-answering systems enables the effective delivery of services to all in agriculture. This approach can rapidly advance the scientific and precision-based development of agricultural production. Existing agricultural Q&A systems lack enough semantic understanding of complex symptoms, while general-purpose large language models (LLMs) produce factual hallucinations due to incomplete training data coverage. The research aims to address the issues of insufficient scale and low quality in the construction of knowledge bases in the agricultural field. [Methods] First, disease and pest data were collected along for five typical crops: wheat, rice, corn, potatoes, and cotton. Using manual verification methods, outliers were precisely identified and removed, ultimately yielding 87 901 unstructured data entries. Then, a few-shot learning model was employed to extract entities defined in the pattern layer, and these entities were aligned with the semantic vectors of Bert and LLM prompt engineering, ultimately yielding a triplet knowledge base of 916 239 entries for knowledge retrieval. A knowledge retrieval-augmented LLM approach for intelligent Q&A on crop diseases and pests was proposed, specifically the adaptive hybrid retrieval-augmented generation (AHR-RAG) approach. Firstly, an overlapping mechanism was introduced during fixed-length segmentation to mitigate semantic fragmentation. Simultaneously, vector semantic similarity was used to match highly related text blocks with the topic for optimization and storage. Then, single-hop and multi-hop retrieval were designed based on the complexity of the problem. Single-hop retrieval used the BM25 algorithm to match information extracted from the query with document content in the Elasticsearch index, feeding the results into the LLM to enhance answer generation. Multi-hop retrieval first converted user queries into structured conditions and semantic vector representations. Results retrieved from different knowledge bases were then fused using reciprocal rank fusion (RRF) and fed into the LLM. [Results and Discussions] The proposed method was experimentally compared with multiple baseline approaches, including different query types and complexity queries. The results demonstrated that the proposed method achieved accuracy and F₁ improvements of 0.193 and 0.170, respectively, on the Qwen1.5-7B-Chat model. Compared to the improved methods Self-RAG and Adaptive-RAG, AHR-RAG maintained low response times while achieving F₁ improvements of 0.05 and 0.021, respectively, with an accuracy as high as 0.896. For multi-type question-answering tasks, compared to the Naive-RAG method that relied solely on prior knowledge, the AHR-RAG approach achieved accuracy improvements of 0.231, 0.123, and 0.157 for comparison, judgment and selection query types, respectively. For parsing complex semantics, AHR-RAG also demonstrated significant advantages. In single-hop queries, its accuracy reached 0.921, representing a 0.029 improvement over Adaptive-RAG. In multi-hop query scenarios, its accuracy reached 0.748, achieving gains of 0.082 and 0.059 over Self-RAG and Adaptive-RAG respectively. In retrieval-augmented generation, AHR-RAG achieved a 0.013 increase in accuracy and a 0.009 improvement in F₁ by optimizing prompt strategies, compared to directly feeding retrieval results to the model for output. [Conclusions] This research demonstrates strong adaptability to diverse query types and excels at reasoning complex queries such as multi-hop searches. It delivers significant advantages in answer generation accuracy, relevance, and comprehensiveness, producing responses with enhanced logical coherence and richer content. Future work will explore the integration of multimodal knowledge bases.

[Objective] Preventing and containing leaf diseases is a critical component of tea production, and accurate identification and localization of symptoms are essential for modern, automated plantation management. Field inspection in tea gardens poses distinctive challenges for vision-based detection algorithms: targets appeared at widely varying scales and morphologies under complex backgrounds and unfixed acquisition distances, which easily misled detectors. Models trained on standardized datasets with uniform distance and background often underperform, leading to false alarms and missed detections. To support method development under realistic constraints, YOLO-SADMFA (You Only Look Once-Switchable Atrous Dynamic Multi-scale Frequency-aware Adaptive), a detector based on the YOLOv11n backbone was proposed. The architecture aims to preserve fine details during repeated re-sampling (down- and up-sampling), strengthen modeling of lesions at varying scales, and refine multi-scale feature fusion. [Methods] The proposed architecture incorporated additional convolutional, feature extraction, upsampling, and detection head stages to better handle multi-scale representations, and a DMF-Upsample (Dynamic Multi-scale Frequency-aware Upsample) module that performed upsampling through multi-scale feature analysis and dynamic frequency adjustment fusion was introduced. This module enabled efficient multi-scale feature integration while effectively mitigating information loss during up- and down-sampling. Concretely, the DMF-Upsample analyzed multi-frequency responses from adjacent pyramid levels and fused them with dynamically learned frequency-selective weights, which preserved high-frequency lesion boundaries and textures while retaining low-frequency contextual structure such as leaf contours and global shading. A lightweight gating mechanism estimates per-location and per-channel coefficients to regulate the contribution of different bands, and a residual bypass preserved identity information to further reduce aliasing and oversmoothing introduced by repeated resampling. Furthermore, the baseline C3k2 block was replaced with a switchable atrous convolution (SAConv) module, which enhanced multi-scale feature capture by combining outputs from different dilation rates and incorporates a weight locking mechanism to improve model stability and performance. In practice, the SAConv aggregated parallel atrous branched at multiple dilation factors through learned coefficients under weight locking, which expanded the effective receptive field without sacrificing spatial resolution and suppressed gridding artifacts, while incurring modest parameter overhead. Lastly, an adaptive spatial feature fusion (ASFF) mechanism was integrated into the detection head, forming an ASFF-Head that learned spatially varying fusion weights across different feature scales, effectively filters conflicting information, and strengthens the model's robustness and overall detection accuracy. Together, these components formed a deeper yet efficient multi-scale pathway suited to complex field scenes. [Results and Discussions] Compared with the original YOLOv11n model, YOLO-SADMFA improved precision, recall, and mAP by 4.4, 8.4, and 3.7 percentage points, respectively, indicating more reliable identification and localization across diverse field scenes. The detector was particularly effective for multi-scale targets where the lesion area occupied approximately 10%－65% of the image, reflecting the variability introduced by unfixed acquisition distance during tea garden patrols. Under low illumination and in complex backgrounds with occlusions and clutter, it maintained stable performance, reduced both missed detections and false alarms, and effectively distinguished disease categories with similar morphology and color. On edge computing devices, it sustained about 161 f/s, which met real-time requirements for mobile inspection robots and portable systems. These outcomes demonstrated strengthened robustness to background interference and improved sensitivity at extreme scales, which was consistent with practical demands where the acquisition distance was not fixed. From an ablation perspective, DMF-Upsample preserved high-frequency lesion boundaries while retaining low-frequency structural context after resampling, SAConv expanded receptive fields through multi-dilation aggregation under a weight-locking mechanism, and the ASFF-Head mitigated conflicts among feature pyramids. Their combination yielded cumulative gains in stability and accuracy. Qualitative analyses further supported the quantitative results: Boundary localization improved for small, speckled lesions, large blotches were captured with fewer spurious edges, and distractors such as veins, shadows, and soil textures were less frequently misclassified, confirming the benefits of dynamic multi-scale frequency-aware fusion and adaptive spatial weighting in real field conditions. [Conclusions] The proposed YOLO-SADMFA effectively addressed the multi-scale disease detection challenge in complex tea garden environments, where acquisition distance was not fixed, lesion morphology and color were diverse, and cluttered backgrounds easily caused misjudgments and omissions. It significantly improved detection accuracy and robustness relative to the original YOLOv11n model across a wide range of target scales, and it maintained stable performance under low illumination and complex backgrounds typical of field inspections. It provided reliable technical support for automated tea leaf disease inspection systems by enabling accurate localization and identification of lesions in real operating conditions and by sustaining real-time inference on edge devices suitable for patrol-style deployment.

[Objective] Crop diseases and pests are significant factors restricting global agricultural production. Traditional intelligent recognition technologies predominantly rely on single-modal image data processed by convolutional neural networks (CNNs) or Transformers. However, in complex natural environments, these methods often suffer from insufficient information utilization and limited robustness due to the lack of semantic guidance. Although emerging multimodal approaches like CLIP have introduced textual information, they typically rely on shallow feature alignment in the embedding space without achieving deep semantic interaction or effective feature fusion. Furthermore, the asymmetry between the quantity of image samples and text labels during training poses a challenge for effective cross-modal learning. In this study, a self-supervised adaptive multimodal feature fusion recognition (SAFusion-CLIP) method is proposed, aiming to significantly enhance classification accuracy and model generalization in fine-grained diseases and pests recognition tasks. [Methods] A comprehensive recognition framework was constructed, integrating four key components to achieve deep fusion of visual and textual features. First, prompt engineering was conducted by utilizing large language models (LLMs) combined with authoritative agricultural guides to transform simple category labels into fine-grained pathological semantic descriptions. These descriptions encapsulated morphological details, color gradients, and texture features, with quality verified by BERTScore and ROUGE-L metrics. Second, a cross-modal balanced alignment module was designed to resolve the problem of sample asymmetry between image batches and fixed text labels. This module employed a dot-product attention mechanism to calculate the correlation between image and text projections, applying Softmax normalization to dynamically align image features with their corresponding textual representations. Third, an adaptive fusion mechanism was employed to achieve deep semantic interaction. A gating unit based on the Sigmoid function was designed to calculate a gate value, which dynamically allocated weights to image and text features, allowing the model to adaptively integrate complementary information from both modalities. Finally, a self-supervised feature reconstruction task was introduced to enhance the robustness of feature representation. A simple decoder was utilized to reconstruct the original image and text embeddings from the fused features, and the model was optimized using a composite objective function combining image-text contrastive loss, mean squared error reconstruction loss, and weighted cross-entropy classification loss. [Results and Discussions] Extensive experiments were conducted on the standard PlantVillage dataset, which includes 39 categories covering 14 crop species. The proposed SAFusion-CLIP model achieved a classification accuracy of 99.67%, with precision, recall, and F₁-Score all exceeding 99.00%. Comparative analysis demonstrated that the proposed method significantly outperformed mainstream single-modal and baseline multimodal models, ResNet50 (96.51%), Swin-Transformer (97.48%), and baseline CLIP (98.23%), respectively. Visualization analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) indicated that, unlike single-modal models which were susceptible to background noise or non-specific physical damage, the SAFusion-CLIP model focused more precisely on core lesion areas, effectively suppressing background interference. Furthermore, ablation studies confirmed the effectiveness of the proposed modules, showing that the combination of the self-supervised architecture and the adaptive fusion mechanism resulted in a 2.46 percentage points accuracy improvement over the baseline, validating the necessity of deep feature interaction and reconstruction tasks. [Conclusions] By fusing textual semantics with visual features, the SAFusion-CLIP method effectively overcame the limitations of single-modal recognition. The adaptive fusion mechanism ensured deep interaction between modalities, while the self-supervised reconstruction task significantly enhanced the robustness of feature representation. The experimental results verified that this data-driven approach significantly improves accuracy and generalization capabilities in fine-grained crop disease classification tasks, providing a new and effective solution for precision agricultural prevention and control.

[Significance] The pig industry is a key sector of animal husbandry. With the continuous expansion of farming scale, traditional manual inspection methods can no longer meet the demands of modern production in terms of efficiency, accuracy, and animal welfare. In recent years, intelligent pig monitoring technologies based on multi-source data, such as images, depth information, sensors, and sound, have developed rapidly, providing new solutions for health monitoring, behavior recognition, weight assessment, and physiological state management during the farming process. As a crucial foundation for upgrading the pig industry toward intelligent and precise farming, it is of significant value to systematically review the current research status, application progress, and future trends of the technological system. [Progress] This paper focuses on the main research areas in intelligent pig monitoring, systematically summarizes the commonly used data types and their applications in farming scenarios from the perspective of matching data sources with application objectives. First, research based on infrared images mainly focuses on non-contact acquisition of body temperature information, which is used for disease early warning and health monitoring, offering clear advantages in reducing stress responses and increasing monitoring frequency. Second, visible-light images are widely applied in behavior recognition and health analysis, supporting automated identification and quantification of behaviors such as feeding, resting, and aggression, thereby facilitating dynamic understanding of pig herd behavior patterns and changes. Third, depth images and three-dimensional information demonstrate unique value in body measurement extraction and weight estimation, promoting the development of non-contact, continuous weight monitoring. Fourth, wearable sensors enable continuous monitoring of pig's health, lameness risk, and daily behavioral rhythms by recording physiological data such as body temperature, acceleration, and feeding activity in real time. Finally, audio signals, an emerging data type in recent years, have shown potential in monitoring abnormal sounds such as coughing, providing a new approach for the early detection of respiratory diseases. On this basis, this paper further summarizes the research and application of intelligent detection equipment. Current equipment presents a development trend in two aspects: one focuses on single indicators such as body temperature and weight, characterized by precise collection and rapid feedback; the other integrates multiple functions including image acquisition, body temperature detection, behavior recording, and identity recognition through mobile platforms such as inspection robots, enabling full-scenario and all-weather intelligent detection and improving the automation and refinement level of pig farm management. With the growth of industrial demand, various types of equipment are gradually moving from laboratories to commercialization, providing important support for intelligent breeding. [Conclusions and Prospects] Despite the rapid development of intelligent pig detection technology, multiple challenges still exist. At the data level, interference from lighting, occlusion, and noise in different scenarios can affect the stability of detection results; at the hardware level, some equipment suffers from high costs and needs improvement in reliability; at the model level, differences across pig farms, breeds, and growth stages still lead to insufficient adaptability; at the application level, data continuity, system stability, and equipment maintenance costs in large-scale scenarios require further optimization. These factors collectively restrict the large-scale promotion of intelligent detection technology in the industry. Future research directions will exhibit the following common trends: First, achieving contactless operation and multi-scenario adaptability to minimize disturbance to pigs and enhance stability in complex environments. Second, advancing the integration of multimodal data fusion and deep learning to establish stronger correlations among multi-source data such as images, sensors, and audio. Third, developing individualized health and growth models to provide a scientific basis for precision feeding and management.

[Significance] With the advancement of technology and diversified consumer demands, traditional agriculture is gradually transforming towards information and intelligence. Conducting research on intelligent management and control technologies for facility vegetable production is of great significance for improving vegetable yield and quality, ensuring stable market supply, and promoting high-quality development of the vegetable industry. The purpose of this article is to systematically collate the research status, key technologies and application constraints in the field of intelligent management and control of facility vegetables. By analyzing the development trends of environmental regrlation, growth monitoring and precise management, it provides scientific basis, theoretical support and decision-making references for the intelligent upgrading, technological innovation and policy formulation of Chinese facility vegetable industry, so as to boost the high-quality and sustainable development of facility agriculture. [Progress] This paper systematically analyzes the innovative applications of information technologies such as Internet of Things, block chain, and artificial intelligence in critical domains of facility vegetable production information, including precise regulation of the production environment, intelligent cultivation management and smart storage information management. In terms of precise regulation of the production environment, a temperature and humidity model for the optimal growth environment of tomatoes has been established, primarily utilizing Internet of Things technology. This enables precise monitoring and intelligent control of environmental parameters such as temperature, humidity, light, and carbon dioxide concentration within the facility, creating an optimal environment for vegetable growth. In the field of intelligent cultivation management, the integration of intelligent integrated water and fertilizer equipment, agricultural robotic operation systems, and pest and disease control has optimized the whole-process information-based management, effectively improving cultivation efficiency and vegetable quality. The integrated water and fertilizer systems apply Internet on Things technology to coordinate irrigation and fertilization through digital methods. Agricultural robotic operation systems are based on artificial intelligence, encompassing technologies such as machine learning, deep learning, neural networks and image processing. The pest and disease control section highlights the information-based applications in physical control, biological control and chemical control. In terms of smart storage information management, the application of origin storage preservation technology, intelligent classification and sorting systems, as well as traceability information platforms has significantly enhanced the circulation quality and safety assurance level of vegetables. Specifically, the origin storage preservation technology focuses on the development status of pre-cooling preservation, controlled atmosphere preservation, biological preservation and coating preservation. Intelligent grading and sorting technologies are categorized into non-destructive testing for both the external and internal quality of vegetables. The traceability information platform, leveraging blockchain and large model technologies, enables more intelligent management of facility vegetable production. [Conclusions and Prospects] This paper explores the problems encountered in the development of intelligent management and control technology for protected vegetables, including insufficient accuracy and stability of sensors, lagging regulatory decision-making, lack of equipment coordination mechanisms, poor integration of pest and disease control, fragmentation of information in the whole process of storage, difficulty in quality traceability, and lagging risk warning. Corresponding countermeasures and suggestions are proposed as follows: optimization of hardware, multi-technology integration to support precise perception and intelligent regulation, enhancement of equipment coordination and optimization, integration of pest and disease control, and construction of a virtual-real interactive storage management system through the integration of digital twins and metaverse. Finally, the paper prospects the future development direction of facility vegetable in precise control of production environment, cultivation management information, and storage information control.

[Significance] In alignment with the national germplasm security strategy, current research efforts are accelerating the adoption of precision breeding in sheep. Within the whole-genome selection, accurate phenotyping of body morphometrics is critical for assessing growth performance and breeding value. Traditional manual measurements are inefficient, prone to human error, and may cause stress to sheep, limiting their suitability for precision sheep management. By summarizing the applications of sheep body size measurement technologies and analyzing their development directions, this paper provides theoretical references and practical guidance for the research and application of non contact sheep body size measurement. [Progress] This review synthesizes progress across three principal methodological paradigms: two-dimensional (2D) image-based techniques, three-dimensional (3D) point cloud-based approaches, and integrated 2D-3D fusion systems. 2D methods, employing either handcrafted geometric features or deep learning-based keypoint detector algorithms, are cost-effective and operationally simple but sensitive to variation in imaging conditions and unable to capture critical circumference metrics. 3D point-cloud approaches enable precise reconstruction of full animal morphology, supporting comprehensive body-size acquisition with higher accuracy, yet face challenges including high hardware costs, complex data workflows, and sensitivity to posture variability. Hybrid 2D-3D fusion systems combine semantic richness from RGB imagery with geometric completeness from point clouds. Having been effectively validated in other livestock specise, e.g., cattle and pigs, these fusion systems have demonstrated excellent performance, providing important technical references and practical insights for sheep body size measurement. [Conclusions and Prospects] Firstly, future research should focus on constructing large-scale, high-quality datasets for sheep body size measurement that encompass diverse breeds, growth stages, and environmental conditions, thereby enhancing model robustness and generalization. Secondly, the development of lightweight artificial intelligence models is essential. Techniques such as model compression, quantization, and algorithmic optimization can substantially reduce computational complexity and storage requirements, facilitating deployment in resource-constrained environments. Thirdly, the 3D point cloud processing pipeline should be streamlined to improve the efficiency of data acquisition, filtering, registration, and segmentation, while promoting the integration of low-cost, high-resilience vision systems into practical farming scenarios. Fourthly, specific emphasis should be placed on improving the accuracy of curved-dimensional measurements, such as chest circumference, abdominal circumference, and shank circumference, through advances in pose standardization, refined 3D segmentation strategies, and multi-modal data fusion. Finally, the cross-fertilization of sheep body size measurement technologies with analogous methods for other livestock species offers a promising pathway for mutual learning and collaborative innovation, accelerating the industrialization of automated sheep morphometric systems and supporting the development of intelligent, data-driven pasture management practices.

[Objective] Acquiring valid data is a critical part for establishing accurate greenhouse prediction models. However, simple averaging and weighted averaging are commonly used to process multi-sensor data in current research, but these methods are often ineffective against sensor noise interference. Additionally, greenhouse temperature and humidity exhibit strong coupling characteristics that necessitate coordinated control strategies. Prevailing studies predominantly train separate models for temperature and humidity prediction, which risks generating physically inconsistent results (e.g., simultaneous high temperature and high humidity), using it as the basis for control may lack reliability. Furthermore, the multi-dimensional environmental factor data in greenhouses has the characteristics of large volume and high computational cost. In the training process of the traditional LSTM model, parameters are manually adjusted based on human experience. When dealing with high-dimensional data, the model's convergence is slow and it is prone to getting stuck in local optima. [Methods] To address multi-point data fusion challenges, the traditional Kalman filtering algorithm was improved by dynamically adjusting the process noise covariance (Q) and observation noise covariance (R), while adaptively assigning weights to multiple sensors based on the innovation. This adaptation was achieved by monitoring the innovation sequence—the difference between observed and predicted measurements. Furthermore, the algorithm utilized the innovation covariance to assign adaptive weights to multiple sensors. Sensors with consistently smaller innovations which indicated higher reliability, were assigned greater weights. This mechanism enabled the system to swiftly identify and mitigate the impact of abnormal sensor readings, thereby ensuring robust and accurate fusion of multi-sensor data and providing a reliable foundation for subsequent model training. To address the strong coupling between temperature and humidity and their collaborative control requirements, a multi-output LSTM-attention model was developed for joint temperature-humidity prediction within a unified architecture. This model employed an attention mechanism to adaptively weight critical environmental factors, thereby resolving physical constraint violations inherent in univariate forecasting approaches. The multi-dimensional nature of greenhouse environmental data often leads to high computational costs during model training. In traditional practices, the hyperparameters of LSTM models were often manually tuned based on experience, a process that was not only inefficient but also prone to suboptimal convergence and local optima traps, especially with high-dimensional data. To overcome this limitation, the grey wolf optimizer (GWO) was integrated to automatically perform hyperparameter optimization search and efficiently search for the optimal combination of key hyperparameters, such as the number of hidden units, learning rate, and dropout rate. [Results and Discussions] The adaptive Kalman filtering algorithm proposed achieved mean absolute deviations (MAD) of 1.59 ℃ and 8.64% for multi-point temperature and humidity fusion, respectively. Compared to the traditional Kalman filter algorithm, these represented reductions of 1.24% and 8.57%. The algorithm enabled swift identification of abnormal sensors and effectively mitigated their impact. When utilizing the fusion results of this algorithm as the model training dataset, the R² values for temperature and humidity predictions reached 98.2% and 99.3%, respectively. This constituted an increase of 4.7 and 4.3 percentage points compared to results obtained using the Kalman filter, demonstrating that the algorithm provided a highly reliable data foundation for model training. Furthermore, the GWO-LSTM-Attention model trained on this data yielded root mean square errors (RMSE) of 0.776 8 and 2.056 4 for temperature and humidity prediction, respectively. Compared to the LSTM and LSTM-Attention time-series prediction models, the temperature RMSE was reduced by 15.6% and 6.6%, while the humidity RMSE saw reductions of 29.2% and 5.7%. This reflects the role of the GWO algorithm in enhancing model generalization capability and convergence efficiency. [Conclusions] The proposed adaptive Kalman fusion algorithm effectively integrates multi-sensor data, demonstrating robustness in handling sensor noise, outliers, and non-stationary environmental fluctuations. For predicting multiple greenhouse environmental factors, the developed GWO-LSTM-Attention model provides reliable forecasts across diverse time horizons. This study can provide a highly accurate prediction tool for greenhouse environment control. The combined prediction results could directly support the coordinated control of ventilation and irrigation equipment in the future, thereby reducing energy consumption.

[Objective] Maize leaf dry biomass is a key trait that reflects plant morphology, growth vigor, and physiological processes including photosynthetic production. Its dynamic changes can effectively characterize the growth status of maize. Accurate estimation of maize leaf dry biomass is crucial for accurately predicting maize yield and informing production management decisions. Extensive research on crop dry biomass estimation indicates that 3D point cloud data characterizing crop morphological structure, along with features derived therefrom, exhibit an extremely high correlation with crop dry biomass. However, traditional dry biomass prediction studies focus primarily on the population canopy scale, and lack effective prediction methods for dry biomass at the plant and organ scales. Research on non-destructive measurement methods for maize leaf dry biomass, based on 3D point clouds and machine learning, the demand is conducted to address for rapid acquisition of organ-level dry biomass information in maize cultivation and management research. [Methods] Maize leaf point cloud data were acquired using three techniques: Multi-view stereo (MVS), LiDAR scanning, and 3D digitalization (DT). The leaf point clouds underwent preprocessing steps that included plant segmentation, denoising, mesh refinement, and uniform subsampling. Subsequently, morphological traits were extracted from the processed data, including leaf length, leaf area, bounding box dimensions, and the number of points contained within the leaf point clouds. Three machine learning methods: random forest (RF), gradient boosting regression tree (GBRT), and support vector regression (SVR), as well as two deep learning methods: convolutional neural network (CNN) and fully connected neural network (FCNN), were employed for predicting maize leaf dry weight. A point cloud-based maize leaf dry biomass prediction model was subsequently developed. This study utilized the mean squared error reduction method inherent to RF and the cumulative improvement method based on decision tree splits in GBRT to rank and visualize feature importance for optimal models. The resulting rankings were then visualized. Simultaneously, Pearson correlation analysis was used to analyze the correlations of the features from the fused dataset (integrating data from the three devices) as well as those from the DT data with maize leaf dry biomass. [Results and Discussions] The results demonstrated that, among the dry biomass prediction models developed in this study, the model based on Laser point cloud data and the FCNN method achieved the highest accuracy, with a mean absolute error (MAE) of 0.08 g, a mean absolute percentage error (MAPE) of 4.60%, a root mean square error (RMSE) of 0.10 g, and a coefficient of determination (R²) of 0.98. In the correlation analysis, the leaf area exhibited the strongest correlation with dry biomass (r = 0.92), followed by the number of points (r = 0.88), leaf width (r = 0.86), and leaf length (r = 0.77). In the feature importance ranking, the leaf area trait consistently ranked within the top two positions, whereas the number of points ranked among the top three in most cases. However, features such as the height of the leaf base above the ground, the horizontal distances from the leaf tip and apex to the stem, and the azimuth angle demonstrated low correlations with dry biomass and low feature importance. [Conclusions] Among all the maize leaf features investigated in this study, size-related traits (such as leaf area, point count, leaf length, and leaf width) had the greatest impact on the accuracy of dry biomass estimation. The utilization of high-resolution 3D point clouds of maize leaves, combined with machine learning methods, enabled a high-accuracy estimation of leaf dry weight and provided a novel approach for the non-destructive measurement of dry biomass in crop organs.

[Objective] As a major grain crop, maize plays a critical role in global food security. The ears of maize serves as a key phenotypic trait, providing essential information on the plant's physiological and agronomic status. Its morphological characteristics, size, and color effectively reflect the plant's growth status and potential yield. Therefore, accurately acquiring images of maize ears in the field across different growth stages is crucial for breeding research and yield prediction. Traditional field detection of maize ears relies heavily on manual labor, which is not only inefficient and labor-intensive but also struggles to meet the high-throughput demands of modern precision breeding programs. There is an urgent need for efficient, automated detection technologies that can operate reliably under real-world field conditions. To address the requirement for efficient acquisition of maize ears phenotypic traits in field breeding work, the objective of this research is to develop a robust object detection solution suitable for large-scale field environments. An improved CornYOLO model based on the YOLO11n (You Only Look Once) architecture was designed to enhance the detection accuracy and efficiency of maize ears in complex field environments. [Methods] Image data were acquired using an unmanned ground vehicle (UGV) equipped with a high-resolution panoramic camera, which traversed multiple experimental plots under varying lighting and growth conditions. A dataset containing 1 152 annotated samples was constructed, covering diverse ear morphologies and occlusion scenarios. Dynamic data augmentation techniques were applied during training to enhance the model's generalization capability. Three key enhancements were introduced to the YOLO11n detection framework. First, a cross stage partial network with dynamic pointwise spatial attention (C2PDA) module was designed to replace the cross stage partial with pointwise spatial attention (C2PSA) module in the YOLO11 backbone network. This module enhanced spatial discriminability and channel sensitivity in feature representation through the collaborative integration of a dynamic channel weighting mechanism and position-aware modeling. It significantly improves the model's performance in identifying maize ears under challenging field conditions such as occlusion of stems and leaves and multi-scale target distribution. Second, the spatial pyramid pooling-fast (SPPF) module in the original model was replaced with an feature refinement module (FRM ) to optimize multi-scale feature fusion. The FRM functions via directional feature decomposition and an adaptive attention mechanism. It captures fine-grained spatial structural information through horizontal and vertical bidirectional pooling and combines spatial-channel cooperative attention for dynamic feature calibration, thereby improving recognition accuracy across varying ear sizes and complex backgrounds. Finally, the unified intersection over union (UIoU) loss function was introduced to optimize bounding box regression accuracy. UIoU is an innovative loss function that emphasizes weight allocation among prediction boxes of different qualities. It adaptively adjusted the weight of each prediction box's loss term based on the IoU value or its monotonic function, assigning higher weights to lower-quality predictions to prioritize their optimization, while reducing weights for high-quality boxes to prevent over-optimization. [Results and Discussions] Experimental results demonstrate that CornYOLO achieved a mAP@50 of 89.3% on the validation set, with the F₁-Score increasing by 2.5 percentage points. Compared to widely used lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv8n, YOLOv10n, real-time detection transformer (RT-DETR) and YOLO13n, CornYOLO showed significantly superior detection performance in complex field environments, with mAP@50 improvements of 2.2, 1.9, 1.8, 5.7, 12.6 and 2.4 percentage points, respectively. These results fully validate that CornYOLO can efficiently and accurately extract maize ear images under field conditions, providing a technical foundation for precise phenotypic evaluation and yield prediction. Furthermore, ablation studies were conducted: Introducing the C2PDA module improved the model's mAP@50 by 0.5 percentage points and the F₁-Score by 0.5 percentage points. However, after incorporating the FRM module, which successfully enhanced multi-scale detection performance and increased the F₁-Score by 1.5 percentage points, the integration of these two modules resulted in the generation of a small number of low-quality detection boxes. The original loss function was inefficient in optimizing such boxes, resulting in no improvement in mAP@50 after the modification. To address this issue, the UIoU loss function was introduced. By dynamically adjusting weight assignments based on prediction quality, it significantly improved the regression performance for low-quality detection boxes, thereby enhancing the localization accuracy and convergence stability of the model in dense target scenarios. The final CornYOLO model exhibited excellent overall performance: Compared to the original YOLO11n, the F₁-Score increased by 2.5 percentage points and mAP@50 improved by 1.1 percentage points. The experimental results fully demonstrate that CornYOLO effectively enhances the detection capability for maize ears in complex field environments compared to the baseline YOLO11n model. [Conclusions] The CornYOLO model proposed in this study incorporates three key components: C2PDA, FRM, and UIoU, which enhances model convergence and localization performance in dense and occluded scenes, enables the model to effectively and precisely identify maize ears under practical conditions, thereby providing reliable technical support for phenotypic analysis and yield prediction in maize breeding. Future work will focus on extending the model to other crop types and further optimizing inference efficiency for real-time deployment on mobile platforms.