[Objective] As agriculture increasingly relies on technological innovations to boost productivity and ensure sustainability, farmers need efficient and accurate tools to aid their decision-making processes. A key challenge in this context is the retrieval of specialized agricultural knowledge, which can be complex and diverse in nature. Traditional agricultural knowledge retrieval systems have often been limited by the modalities they utilize (e.g., text or images alone), which restricts their effectiveness in addressing the wide range of queries farmers face. To address this challenge, a specialized multimodal question-answering system tailored for cabbage cultivation was proposed. The system, named Agri-QA Net, integrates multimodal data to enhance the accuracy and applicability of agricultural knowledge retrieval. By incorporating diverse data modalities, Agri-QA Net aims to provide a holistic approach to agricultural knowledge retrieval, enabling farmers to interact with the system using multiple types of input, ranging from spoken queries to images of crop conditions. By doing so, it helps address the complexity of real-world agricultural environments and improves the accessibility of relevant information. [Methods] The architecture of Agri-QA Net was built upon the integration of multiple data modalities, including textual, auditory, and visual data. This multifaceted approach enables the system to develop a comprehensive understanding of agricultural knowledge, allowed the system to learn from a wide array of sources, enhancing its robustness and generalizability. The system incorporated state-of-the-art deep learning models, each designed to handle one specific type of data. Bidirectional Encoder Representations from Transformers (BERT)'s bidirectional attention mechanism allowed the model to understand the context of each word in a given sentence, significantly improving its ability to comprehend complex agricultural terminology and specialized concepts. The system also incorporated acoustic models for processing audio inputs. These models analyzed the spoken queries from farmers, allowing the system to understand natural language inputs even in noisy, non-ideal environments, which was a common challenge in real-world agricultural settings. Additionally, convolutional neural networks (CNNs) were employed to process images from various stages of cabbage growth. CNNs were highly effective in capturing spatial hierarchies in images, making them well-suited for tasks such as identifying pests, diseases, or growth abnormalities in cabbage crops. These features were subsequently fused in a Transformer-based fusion layer, which served as the core of the Agri-QA Net architecture. The fusion process ensured that each modality—text, audio, and image—contributes effectively to the final model's understanding of a given query. This allowed the system to provide more nuanced answers to complex agricultural questions, such as identifying specific crop diseases or determining the optimal irrigation schedules for cabbage crops. In addition to the fusion layer, cross-modal attention mechanisms and domain-adaptive techniques were incorporated to refine the model's ability to understand and apply specialized agricultural knowledge. The cross-modal attention mechanism facilitated dynamic interactions between the text, audio, and image data, ensuring that the model paid attention to the most relevant features from each modality. Domain-adaptive techniques further enhanced the system's performance by tailoring it to specific agricultural contexts, such as cabbage farming, pest control, or irrigation management. [Results and Discussions] The experimental evaluations demonstrated that Agri-QA Net outperforms traditional single-modal or simple multimodal models in agricultural knowledge tasks. With the support of multimodal inputs, the system achieved an accuracy rate of 89.5%, a precision rate of 87.9%, a recall rate of 91.3%, and an F₁-Score of 89.6%, all of which are significantly higher than those of single-modality models. The integration of multimodal data significantly enhanced the system's capacity to understand complex agricultural queries, providing more precise and context-aware answers. The addition of cross-modal attention mechanisms enabled for more nuanced and dynamic interaction between the text, audio, and image data, which in turn improved the model's understanding of ambiguous or context-dependent queries, such as disease diagnosis or crop management. Furthermore, the domain-adaptive technique enabled the system to focus on specific agricultural terminology and concepts, thereby enhancing its performance in specialized tasks like cabbage cultivation and pest control. The case studies presented further validated the system's ability to assist farmers by providing actionable, domain-specific answers to questions, demonstrating its practical application in real-world agricultural scenarios. [Conclusions] The proposed Agri-QA Net framework is an effective solution for addressing agricultural knowledge questions, especially in the domain of cabbage cultivation. By integrating multimodal data and leveraging advanced deep learning techniques, the system demonstrates a high level of accuracy and adaptability. This study not only highlights the potential of multimodal fusion in agriculture but also paves the way for future developments in intelligent systems designed to support precision farming. Further work will focus on enhancing the model's performance by expanding the dataset to include more diverse agricultural scenarios, refining the handling of dialectical variations in audio inputs, and improving the system's ability to detect rare crop diseases. The ultimate goal is to contribute to the modernization of agricultural practices, offering farmers more reliable and effective tools to solve the challenges in crop management.

[Objective] The current crop management faces the challenges of difficulty in capturing personalized needs and the lack of flexibility in the decision-making process. To address the limitations of conventional precision agriculture systems, optimize key aspects of agricultural production, including crop yield, labor efficiency, and water and fertilizer use, while ensure sustainability and adaptability to diverse farming conditions, in this research, an intelligent decision-making method was presents for personalized vegetable crop water and fertilizer management using large language model (LLM) by integrating user-specific preferences into decision-making processes through natural language interactions. [Methods] The method employed artificial intelligence techniques, combining natural language processing (NLP) and reinforcement learning (RL). Initially, LLM engaged users through structured dialogues to identify their unique preferences related to crop production goals, such as maximizing yield, reducing resource consumption, or balancing multiple objectives. These preferences were then modeled as quantifiable parameters and incorporated into a multi-objective optimization framework. To realize this framework, proximal policy optimization (PPO) was applied within a reinforcement learning environment to develop dynamic water and fertilizer management strategies. Training was conducted in the gym-DSSAT simulation platform, a system designed for agricultural decision support. The RL model iteratively learned optimal strategies by interacting with the simulation environment, adjusting to diverse conditions and balancing conflicting objectives effectively. To refine the estimation of user preferences, the study introduced a two-phase process comprising prompt engineering to guide user responses and adversarial fine-tuning for enhanced accuracy. These refinements ensured that user inputs were reliably transformed into structured decision-making criteria. Customized reward functions were developed for RL training to address specific agricultural goals. The reward functions account for crop yield, resource efficiency, and labor optimization, aligning with the identified user priorities. Through iterative training and simulation, the system dynamically adapted its decision-making strategies to varying environmental and operational conditions. [Results and Discussions] The experimental evaluation highlighted the system's capability to effectively personalize crop management strategies. Using simulations, the method demonstrated significant improvements over traditional approaches. The LLM-based model accurately captured user-specific preferences through structured natural language interactions, achieving reliable preference modeling and integration into the decision-making process. The system's adaptability was evident in its ability to respond dynamically to changes in user priorities and environmental conditions. For example, in scenarios emphasizing resource conservation, water and fertilizer use were significantly reduced without compromising crop health. Conversely, when users prioritized yield, the system optimized irrigation and fertilization schedules to enhance productivity. These results showcased the method's flexibility and its potential to balance competing objectives in complex agricultural settings. Additionally, the integration of user preferences into RL-based strategy development enabled the generation of tailored management plans. These plans aligned with diverse user goals, including maximizing productivity, minimizing resource consumption, and achieving sustainable farming practices. The system's multi-objective optimization capabilities allowed it to navigate trade-offs effectively, providing actionable insights for decision-making. The experimental validation also demonstrated the robustness of the PPO algorithm in training the RL model. The system's strategies were refined iteratively, resulting in consistent performance improvements across various scenarios. By leveraging LLM to capture nuanced user preferences and combining them with RL for adaptive decision-making, the method bridges the gap between generic precision agriculture solutions and personalized farming needs. [Conclusions] This study established a novel framework for intelligent decision-making in agriculture, integrating LLM with reinforcement learning to address personalized crop management challenges. By accurately capturing user-specific preferences and dynamically adapting to environmental and operational variables, the method offers a transformative approach to optimizing agricultural productivity and sustainability. Future work will focus on expanding the system's applicability to a wider range of crops and environmental contexts, enhancing the interpretability of its decision-making processes, and facilitating integration with real-world agricultural systems. These advancements aim to further refine the precision and impact of intelligent agricultural decision-making systems, supporting sustainable and efficient farming practices globally.

[Objective] The rapid advancement of large language models (LLMs) has positioned them as a promising novel research paradigm in smart agriculture, leveraging their robust cognitive understanding and content generative capabilities. However, due to the lack of domain-specific agricultural knowledge, general LLMs often exhibit factual errors or incomplete information when addressing specialized queries, which is particularly prominent in agricultural applications. Therefore, enhancing the adaptability and response quality of LLMs in agricultural applications has become an important research direction. [Methods] To improve the adaptability and precision of LLMs in the agricultural applications, an innovative approach named the knowledge graph-guided agricultural LLM (KGLLM) was proposed. This method integrated information entropy for effective knowledge filtering and applied explicit constraints on content generation during the decoding phase by utilizing semantic information derived from an agricultural knowledge graph. The process began by identifying and linking key entities from input questions to the agricultural knowledge graph, which facilitated the formation of knowledge inference paths and the development of question-answering rationales. A critical aspect of this approach was ensuring the validity and reliability of the external knowledge incorporated into the model. This was achieved by evaluating the entropy difference in the model's outputs before and after the introduction of each piece of knowledge. Knowledge that didn't enhance the certainty of the answers was systematically filtered out. The knowledge paths that pass this entropy evaluation were used to adjust the token prediction probabilities, prioritizing outputs that were closely aligned with the structured knowledge. This allowed the knowledge graph to exert explicit guidance over the LLM's outputs, ensuring higher accuracy and relevance in agricultural applications. [Results and Discussions] The proposed knowledge graph-guided technique was implemented on five mainstream general-purpose LLMs, including open-source models such as Baichuan, ChatGLM, and Qwen. These models were compared with state-of-the-art knowledge graph-augmented generation methods to evaluate the effectiveness of the proposed approach. The results demonstrate that the proposed knowledge graph-guided approach significantly improved several key performance metrics of fluency, accuracy, factual correctness, and domain relevance. Compared to GPT-4o, the proposed method achieved notable improvements by an average of 2.592 3 in Mean BLEU, 2.815 1 in ROUGE, and 9.84% in BertScore. These improvements collectively signify that the proposed approach effectively leverages agricultural domain knowledge to refine the outputs of general-purpose LLMs, making them more suitable for agricultural applications. Ablation experiments further validated that the knowledge-guided agricultural LLM not only filtered out redundant knowledge but also effectively adjusts token prediction distributions during the decoding phase. This enhanced the adaptability of general-purpose LLMs in agriculture contexts and significantly improves the interpretability of their responses. The knowledge filtering and knowledge graph-guided model decoding method proposed in this study, which was based on information entropy, effectively identifies and selects knowledge that carried more informational content through the comparison of information entropy.Compared to existing technologies in the agricultural field, this method significantly reduced the likelihood of "hallucination" phenomena during the generation process. Furthermore, the guidance of the knowledge graph ensured that the model's generated responses were closely related to professional agricultural knowledge, thereby avoiding vague and inaccurate responses generated from general knowledge. For instance, in the application of pest and disease control, the model could accurately identify the types of crop diseases and corresponding control measures based on the guided knowledge path, thereby providing more reliable decision support. [Conclusions] This study provides a valuable reference for the construction of future agricultural large language models, indicating that the knowledge graphs guided mehtod has the potential to enhance the domain adaptability and answer quality of models. Future research can further explore the application of similar knowledge-guided strategies in other vertical fields to enhance the adaptability and practicality of LLMs across various professional domains.

[Objective] Intelligent services of agricultural knowledge have emerged as a current hot research domain, serving as a significant support for the construction of smart agriculture. The platform "China Agricultural Technology Extension" provides users with efficient and convenient agricultural information consultation services via mobile terminals, and has accumulated a vast amount of Q&A data. These data are characterized by a huge volume of information, rapid update and iteration, and a high degree of redundancy, resulting in the platform encountering issues such as frequent repetitive questions, low timeliness of problem responses, and inaccurate information retrieval. There is an urgent requirement for a high-quality text semantic similarity calculation approach to confront these challenges and effectively enhance the information service efficiency and intelligent level of the platform. In view of the problems of incomplete feature extraction and lack of short agro-text annotation data sets in existing text semantic similarity calculation models, a semantic similarity calculation model for short agro-text, namely CWPT-SBERT, based on transfer learning and BERT pre-training model, was proposed. [Methods] CWPT-SBERT was based on Siamese architecture with identical left and right sides and shared parameters, which had the advantages of low structural complexity and high training efficiency. This network architecture effectively reduced the consumption of computational resources by sharing parameters and ensures that input texts were compared in the same feature space. CWPT-SBERT consisted of four main parts: Semantic enhancement layer, embedding layer, pooling layer, and similarity measurement layer. The CWPT method based on the word segmentation unit was proposed in the semantic enhancement layer to further divide Chinese characters into more fine-grained sub-units maximizing the semantic features in short Chinese text and effectively enhancing the model's understanding of complex Chinese vocabulary and character structures. In the embedding layer, a transfer learning strategy was used to extract features from agricultural short texts based on SBERT. It captured the semantic features of Chinese text in the general domain, and then generated a more suitable semantic feature vector representation after fine-tuning. Transfer learning methods to train models on large-scale general-purposed domain annotation datasets solve the problem of limited short agro-text annotation datasets and high semantic sparsity. The pooling layer used the average pooling strategy to map the high-dimensional semantic vector of Chinese short text to a low-dimensional vector space. The similarity measurement layer used the cosine similarity calculation method to measure the similarity between the semantic feature vector representations of the two output short texts, and the computed similarity degree was finally input into the loss function to guide model training, optimize model parameters, and improve the accuracy of similarity calculation. [Results and Discussions] For the task of calculating semantic similarity in agricultural short texts, on a dataset containing 19 968 pairs of short ago-texts, the CWPT-SBERT model achieved an accuracy rate of 97.18% and 96.93%, a recall rate of 97.14%, and an F₁-Score value of 97.04%, which are higher than 12 models such as TextCNN_Attention, MaLSTM and SBERT. By analyzing the Pearson and Spearman coefficients of CWPT-SBERT, SBERT, SALBERT and SRoBERTa trained on short agro-text datasets, it could be observed that the initial training value of the CWPT-SBERT model was significantly higher than that of the comparison models and was close to the highest value of the comparison models. Moreover, it exhibited a smooth growth trend during the training process, indicating that CWPT-SBERT had strong correlation, robustness, and generalization ability from the initial state. During the training process, it could not only learn the features in the training data but also effectively apply these features to new domain data. Additionally, for ALBERT, RoBERTa and BERT models, fine-tuning training was conducted on short agro-text datasets, and optimization was performed by utilizing the morphological structure features to enrich text semantic feature expression. Through ablation experiments, it was evident that both optimization strategies could effectively enhance the performance of the models. By analyzing the attention weight heatmap of Chinese character morphological structure, the importance of Chinese character radicals in representing Chinese character attributes was highlighted, enhancing the semantic representation of Chinese characters in vector space. There was also complex correlation within the morphological structure of Chinese characters. [Conclusions] CWPT-SBERT uses transfer learning methods to solve the problem of limited short agro-text annotation datasets and high semantic sparsity. By leveraging the Chinese-oriented word segmentation method CWPT to break down Chinese characters, the semantic representation of word vectors is enhanced, and the semantic feature expression of short texts is enriched. CWPT-SBERT model has high accuracy of semantic similarity on small-scale short agro-text and obvious performance advantages, which provides an effective technical reference for semantic intelligence matching.

[Objective] Chinese kiwifruit texts exhibit unique dual-dimensional characteristics. The cross-paragraph dependency is complex semantic structure, whitch makes it challenging to capture the full contextual relationships of entities within a single paragraph, necessitating models capable of robust cross-paragraph semantic extraction to comprehend entity linkages at a global level. However, most existing models rely heavily on local contextual information and struggle to process long-distance dependencies, thereby reducing recognition accuracy. Furthermore, Chinese kiwifruit texts often contain highly nested entities. This nesting and combination increase the complexity of grammatical and semantic relationships, making entity recognition more difficult. To address these challenges, a novel named entity recognition (NER) method, KIWI-Coord-Prune(kiwifruit-CoordKIWINER-PruneBi-LSTM) was proposed in this research, which incorporated dual-dimensional information processing and pruning techniques to improve recognition accuracy. [Methods] The proposed KIWI-Coord-Prune model consisted of a character embedding layer, a CoordKIWINER layer, a PruneBi-LSTM layer, a self-attention mechanism, and a CRF decoding layer, enabling effective entity recognition after processing input character vectors. The CoordKIWINER and PruneBi-LSTM modules were specifically designed to handle the dual-dimensional features in Chinese kiwifruit texts. The CoordKIWINER module applied adaptive average pooling in two directions on the input feature maps and utilized convolution operations to separate the extracted features into vertical and horizontal branches. The horizontal and vertical features were then independently extracted using the Criss-Cross Attention (CCNet) mechanism and Coordinate Attention (CoordAtt) mechanism, respectively. This module significantly enhanced the model's ability to capture cross-paragraph relationships and nested entity structures, thereby generating enriched character vectors containing more contextual information, which improved the overall representation capability and robustness of the model. The PruneBi-LSTM module was built upon the enhanced dual-dimensional vector representations and introduced a pruning strategy into Bi-LSTM to effectively reduce redundant parameters associated with background descriptions and irrelevant terms. This pruning mechanism not only enhanced computational efficiency while maintaining the dynamic sequence modeling capability of Bi-LSTM but also improved inference speed. Additionally, a dynamic feature extraction strategy was employed to reduce the computational complexity of vector sequences and further strengthen the learning capacity for key features, leading to improved recognition of complex entities in kiwifruit texts. Furthermore, the pruned weight matrices become sparser, significantly reducing memory consumption. This made the model more efficient in handling large-scale agricultural text-processing tasks, minimizing redundant information while achieving higher inference and training efficiency with fewer computational resources. [Results and Discussions] Experiments were conducted on the self-built KIWIPRO dataset and four public datasets: People's Daily, ClueNER, Boson, and ResumeNER. The proposed model was compared with five advanced NER models: LSTM, Bi-LSTM, LR-CNN, Softlexicon-LSTM, and KIWINER. The experimental results showed that KIWI-Coord-Prune achieved F₁-Scores of 89.55%, 91.02%, 83.50%, 83.49%, and 95.81%, respectively, outperforming all baseline models. Furthermore, controlled variable experiments were conducted to compare and ablate the CoordKIWINER and PruneBi-LSTM modules across the five datasets, confirming their effectiveness and necessity. Additionally, the impact of different design choices was explored for the CoordKIWINER module, including direct fusion, optimized attention mechanism fusion, and network structure adjustment residual optimization. The experimental results demonstrated that the optimized attention mechanism fusion method yielded the best performance, which was ultimately adopted in the final model. These findings highlight the significance of properly designing attention mechanisms to extract dual-dimensional features for NER tasks. Compared to existing methods, the KIWI-Coord-Prune model effectively addressed the issue of underutilized dual-dimensional information in Chinese kiwifruit texts. It significantly improved entity recognition performance for both overall text structures and individual entity categories. Furthermore, the model exhibited a degree of generalization capability, making it applicable to downstream tasks such as knowledge graph construction and question-answering systems. [Conclusions] This study presents an novel NER approach for Chinese kiwifruit texts, which integrating dual-dimensional information extraction and pruning techniques to overcome challenges related to cross-paragraph dependencies and nested entity structures. The findings offer valuable insights for researchers working on domain-specific NER and contribute to the advancement of agriculture-focused natural language processing applications. However, two key limitations remain: 1) The balance between domain-specific optimization and cross-domain generalization requires further investigation, as the model's adaptability to non-agricultural texts has yet to be empirically validated; 2) the multilingual applicability of the model is currently limited, necessitating further expansion to accommodate multilingual scenarios. Future research should focus on two key directions: 1) Enhancing domain robustness and cross-lingual adaptability by incorporating diverse textual datasets and leveraging pre-trained multilingual models to improve generalization, and 2) Validating the model's performance in multilingual environments through transfer learning while refining linguistic adaptation strategies to further optimize recognition accuracy.

[Significance] The fluctuations in the supply, consumption, and prices of agricultural products directly affect market monitoring and early warning systems. With the ongoing transformation of China's agricultural production methods and market system, advancements in data acquisition technologies have led to an explosive growth in agricultural data. However, the complexity of the data, the narrow applicability of existing models, and their limited adaptability still present significant challenges in monitoring and forecasting the interlinked dynamics of multiple agricultural products. The efficient and accurate forecasting of agricultural market trends is critical for timely policy interventions and disaster management, particularly in a country with a rapidly changing agricultural landscape like China. Consequently, there is a pressing need to develop deep learning models that are tailored to the unique characteristics of Chinese agricultural data. These models should enhance the monitoring and early warning capabilities of agricultural markets, thus enabling precise decision-making and effective emergency responses. [Methods] An integrated forecasting methodology was proposed based on deep learning techniques, leveraging multi-dimensional agricultural data resources from China. The research introduced several models tailored to different aspects of agricultural market forecasting. For production prediction, a generative adversarial network and residual network collaborative model (GAN-ResNet) was employed. For consumption forecasting, a variational autoencoder and ridge regression (VAE-Ridge) model was used, while price prediction was handled by an Adaptive-Transformer model. A key feature of the study was the adoption of an "offline computing and visualization separation" strategy within the Chinese agricultural monitoring and early warning system (CAMES). This strategy ensures that model training and inference are performed offline, with the results transmitted to the front-end system for visualization using lightweight tools such as ECharts. This approach balances computational complexity with the need for real-time early warnings, allowing for more efficient resource allocation and faster response times. The corn, tomato, and live pig market data used in this study covered production, consumption and price data from 1980 to 2023, providing comprehensive data support for model training. [Results and Discussions] The deep learning models proposed in this study significantly enhanced the forecasting accuracy for various agricultural products. For instance, the GAN-ResNet model, when used to predict maize yield at the county level, achieved a mean absolute percentage error (MAPE) of 6.58%. The VAE-Ridge model, applied to pig consumption forecasting, achieved a MAPE of 6.28%, while the Adaptive-Transformer model, used for tomato price prediction, results in a MAPE of 2.25%. These results highlighted the effectiveness of deep learning models in handling complex, nonlinear relationships inherent in agricultural data. Additionally, the models demonstrate notable robustness and adaptability when confronted with challenges such as sparse data, seasonal market fluctuations, and heterogeneous data sources. The GAN-ResNet model excels in capturing the nonlinear fluctuations in production data, particularly in response to external factors such as climate conditions. Its capacity to integrate data from diverse sources—including weather data and historical yield data—made it highly effective for production forecasting, especially in regions with varying climatic conditions. The VAE-Ridge model addressed the issue of data sparsity, particularly in the context of consumption data, and provided valuable insights into the underlying relationships between market demand, macroeconomic factors, and seasonal fluctuations. Finally, the Adaptive-Transformer model stand out in price prediction, with its ability to capture both short-term price fluctuations and long-term price trends, even under extreme market conditions. [Conclusions] This study presents a comprehensive deep learning-based forecasting approach for agricultural market monitoring and early warning. The integration of multiple models for production, consumption, and price prediction provides a systematic, effective, and scalable tool for supporting agricultural decision-making. The proposed models demonstrate excellent performance in handling the nonlinearities and seasonal fluctuations characteristic of agricultural markets. Furthermore, the models' ability to process and integrate heterogeneous data sources enhances their predictive power and makes them highly suitable for application in real-world agricultural monitoring systems. Future research will focus on optimizing model parameters, enhancing model adaptability, and expanding the system to incorporate additional agricultural products and more complex market conditions. These improvements will help increase the stability and practical applicability of the system, thus further enhancing its potential for real-time market monitoring and early warning capabilities.

[Objective] As a key model of smart agriculture, the unmanned smart farm aims to develop a highly intelligent and automated system for high grain yields. This research uses the "1.5-Ton grain per Mu" farm in Dezhou city, Shandong province, as the experimental site, targeting core challenges in large-scale smart agriculture and exploring construction and service models for such farms. [Methods] The "1.5-Ton grain per Mu" unmanned smart farm comprehensively utilized information technologies such as the internet of things (IoT) and big data to achieve full-chain integration and services for information perception, transmission, mining, and application. The overall construction architecture consisted of the perception layer, transmission layer, processing layer, and application layer. This architecture enabled precise perception, secure transmission, analysis and processing, and application services for farm data. A perception system for the unmanned smart farm of wheat was developed, which included a digital perception network and crop phenotypic analysis. The former achieved precise perception, efficient transmission, and precise measurement and control of data information within the farm through perception nodes, self-organizing networks, and edge computing core processing nodes. Phenotypic analysis utilized methods such as deep learning to extract phenotypic characteristics at different growth stages, such as the phenological classification of wheat and wheat ear length. An intelligent controlled system had been developed. The system consisted of an intelligent agricultural machinery system, a field irrigation system, and an aerial pesticided application system. The intelligent agricultural machinery system was composed of three parts: the basic layer, decision-making layer, and application service layer. They were responsible for obtaining real-time status information of agricultural machinery, formulating management decisions for agricultural machinery, and executing operational commands, respectively. Additionally, appropriate agricultural machinery models and configuration references were provided. A refined irrigation scheme was designed based on the water requirements and soil conditions at different developmental stages of wheat. And, an irrigation control algorithm based on fuzzy PID was proposed. Finally, relying on technologies such as multi-source data fusion, distributed computing, and geographic information system (GIS), an intelligent management and control platform for the entire agricultural production process was established. [Results and Discussions] The digital perception network enabled precise sensing and networked transmission of environmental information within the farm. The data communication quality of the sensor network remained above 85%, effectively ensuring data transmission quality. The average relative error in extracting wheat spike length information based on deep learning algorithms was 1.24%. Through the coordinated operation of intelligent control system, the farm achieved lean and unmanned production management, enabling intelligent control throughout the entire production chain, which significantly reduced labor costs and improved the precision and efficiency of farm management. The irrigation model not only saved 20% of irrigation water but also increased the yield of "Jinan 17" and "Jimai 44" by 10.18% and 7%, respectively. Pesticide application through spraying drones reduced pesticide usage by 55%. The big data platform provided users with production guidance services such as meteorological disaster prediction, optimal sowing time, environmental prediction, and water and fertilizer management through intelligent scientific decision support, intelligent agricultural machinery operation, and producted quality and safety traceability modules, helping farmers manage their farms scientifically. [Conclusions] The study achieved comprehensive collection of environmental information within the farm, precise phenotypic analysis, and intelligent control of agricultural machinery, irrigation equipment, and other equipment. Additionally, it realized digital services for agricultural management through a big data platform. The development path of the "1.5-Ton grain per Mu" unmanned smart farm can provid references for the construction of smart agriculture.

[Objective] Plant leaf shape is an important part of plant architectural model. Establishment of a three-dimensional structural model of leaves may assist simulating and analyzing plant growth. However, existing leaf modeling approaches lack interpretability, invertibility, and operability, which limit the estimation of model parameters, the simulation of leaf shape, the analysis and interpretation of leaf physiology and growth state, and model reusage. Aiming at the interoperability between three-dimensional structure representation and mathematical model parameters, this study paid attention to three aspects in wheat leaf shape parametric reconstruction: (1) parameter-driven model structure, (2) model parameter inversion, and (3) parameter dynamic mapping during growth. Based on this, a set of parameter-driven and point cloud inversion model for wheat leaf interoperability was proposed in this study. [Methods] A parametric surface model of a wheat leaf with seven characteristic parameters by using parametric modeling technology was built, and the forward parametric construction of the wheat leaf structure was realized. Three parameters, maximum leaf width, leaf length, and leaf shape factor, were used to describe the basic shape of the blade on the leaf plane. On this basis, two parameters, namely the angle between stems and leaves and the curvature degree, were introduced to describe the bending characteristics of the main vein of the blade in the three-dimensional space. Two parameters, namely the twist angle around the axis and the twist deviation angle around the axis, were introduced to represent the twisted structure of the leaf blade along the vein. The reverse parameter estimation module was built according to the surface model. The point cloud was divided by the uniform segmentation method along the Y-axis, and the veins were fit by a least squares regression method. Then, the point cloud was re-segmented according to the fit vein curve. Subsequently, the rotation angle was precisely determined through the segment-wise transform estimation method, with all parameters being optimally fit using the RANSAC regression algorithm. To validate the reliability of the proposed methodology, a set of sample parameters was randomly generated, from which corresponding sample point clouds were synthesized. These sample point clouds were then subjected to estimation using the described method. Then error analyzing was carried out on the estimation results. Three-dimensional imaging technology was used to collect the point clouds of Zhengmai 136, Yangmai 34, and Yanmai 1 samples. After noise reduction and coordinate registration, the model parameters were inverted and estimated, and the reconstructed point clouds were produced using the parametric model. The reconstruction error was validated by calculating the dissimilarity, represented by the Chamfer Distance, between the reconstructed point cloud and the measured point cloud. [Results and Discussions] The model could effectively reconstruct wheat leaves, and the average deviation of point cloud based parametric reconstruction results was about 1.2 mm, which had a high precision. Parametric modeling technology based on prior knowledge and point cloud fitting technology based on posterior data was integrated in this study to construct a digital twin model of specific species at the 3D structural level. Although some of the detailed characteristics of the leaves were moderately simplified, the geometric shape of the leaves could be highly restored with only a few parameters. This method was not only simple, direct and efficient, but also had more explicit geometric meaning of the obtained parameters, and was both editable and interpretable. In addition, the practice of using only tools such as rulers to measure individual characteristic parameters of plant organs in traditional research was abandoned in this study. High-precision point cloud acquisition technology was adopted to obtain three-dimensional data of wheat leaves, and pre-processing work such as point cloud registration, segmentation, and annotation was completed, laying a data foundation for subsequent research. [Conclusions] There is interoperability between the reconstructed model and the point cloud, and the parameters of the model can be flexibly adjusted to generate leaf clusters with similar shapes. The inversion parameters have high interpretability and can be used for consistent and continuous estimation of point cloud time series. This research is of great value to the simulation analysis and digital twinning of wheat leaves.

[Objective] Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming. However, current prediction methods predominantly rely on empirical, mechanistic, or learning-based models that utilize either images data or environmental data. These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively. [Methods] To address this limitation, a two-stage phenotypic feature extraction (PFE) model based on deep learning algorithm of recurrent neural network (RNN) and long short-term memory (LSTM) was developed. The model integrated environment and plant information to provide a holistic understanding of the growth process, emploied phenotypic and temporal feature extractors to comprehensively capture both types of features, enabled a deeper understanding of the interaction between tomato plants and their environment, ultimately leading to highly accurate predictions of growth height. [Results and Discussions] The experimental results showed the model's effectiveness: When predicting the next two days based on the past five days, the PFE-based RNN and LSTM models achieved mean absolute percentage error (MAPE) of 0.81% and 0.40%, respectively, which were significantly lower than the 8.00% MAPE of the large language model (LLM) and 6.72% MAPE of the Transformer-based model. In longer-term predictions, the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead, the PFE-RNN model continued to outperform the other two baseline models, with MAPE of 2.66% and 14.05%, respectively. [Conclusions] The proposed method, which leverages phenotypic-temporal collaboration, shows great potential for intelligent, data-driven management of tomato cultivation, making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.

[Objective] In crop cultivation and production, pests have gradually become one of the main issues affecting agricultural yield. Traditional models often focus on achieving high accuracy, however, to facilitate model application, lightweighting is necessary. The targets in yellow sticky trap images are often very small with low pixel resolution, so modifications in network structure, loss functions, and lightweight convolutions need to adapt to the detection of small-object pests. Ensuring a balance between model lightweighting and small-object pest detection is particularly important. To improve the detection accuracy of small target pests on sticky trap images from multi-source scenarios, a lightweight detection model named MobileNetV4+VN-YOLOv5s was proposed in this research to detect two main small target pests in agricultural production, whiteflies and thrips. [Methods] In the backbone layer of MobileNetV4+VN-YOLOv5s, an EM block constructed with the MobileNetV4 backbone network was introduced for detecting small, high-density, and overlapping targets, making it suitable for deployment on mobile devices. Additionally, the Neck layer of MobileNetV4+VN-YOLOv5s incorporates the GSConv and VoV-GSCSP modules to replace regular convolutional modules with lightweight design, effectively reducing the parameter size of the model while improving detection accuracy. Lastly, a normalized wasserstein distance (NWD)loss function was introduced into the framework to enhance the sensitivity for low-resolution small target pests. Extensive experiments including state-of-the-art comparison, ablation evaluation, performance analysis on image splitting, pest density and multi-source data were conducted. [Results and Discussions] Through ablation tests, it was concluded that the EM module and the VoV-GSCSP convolution module had significant effects in reducing the model parameter size and frame rate, the NWD loss function significantly improved the mean average precision (mAP) of the model. By comparing tests with different loss functions, the NWD loss function improves the mAP by 6.1, 10.8 and 8.2 percentage compared to the DIoU, GIoU and EIoU loss functions, respectively, so the addition of the NWD loss function achieved good results. Comparative performance tests were detected wiht different light weighting models, the experimental results showed that the mAP of the proposed MobileNetV4+VN-YOLOv5s model in three scenarios (Indoor, Outdoor, Indoor&Outdoor) was 82.5%, 70.8%, and 74.7%, respectively. Particularly, the MobileNetV4+VN-YOLOv5s model had a parameter size of only 4.2 M, 58% of the YOLOv5s model, the frame rate was 153.2 fps, an increase of 6.0 fps compared to the YOLOv5s model. Moreover, the precision and mean average precision reach 79.7% and 82.5%, which were 5.6 and 8.4 percentage points higher than the YOLOv5s model, respectively. Comparative tests were conducted in the upper scenarios based on four splitting ratios: 1×1, 2×2, 5×5, and 10×10. The most superior was the result by using 5×5 ratio in indoor scenario, and the mAP of this case reached 82.5%. The mAP of the indoor scenario was the highest in the low-density case, reaching 83.8%, and the model trained based on the dataset from indoor condition achieves the best performance. Comparative tests under different densities of pest data resulted in a decreasing trend in mAP from low to high densities for the MobileNetV4+VN-YOLOv5s model in the three scenarios. Based on the comparison of the experimental results of different test sets in different scenarios, all three models achieved the best detection accuracy on the IN dataset. Specifically, the IN-model had the highest mAP at 82.5%, followed by the IO-model. At the same time, the detection performance showed the same trend across all three test datasets: The IN model performed the best, followed by the IO-model, and the OUT-model performed the lowest. By comparing the tests with different YOLO improvement models, it was concluded that MobileNetV4+VN-YOLOv5s had the highest mAP, EVN-YOLOv8s was the second highest, and EVN-YOLOv11s was the lowest. Besides, after deploying the model to the Raspberry Pi 4B motherboard, it was concluded that the detection results of the YOLOv5s model had more misdetections and omissions than those of the MobileNetV4+VN-YOLOv5s model, and the time of the model was shortened by about 33% compared to that of the YOLOv5s model, which demonstrated that the model had a good prospect of being deployed in the application. [Conclusions] The MobileNetV4+VN-YOLOv5s model proposed in this study achieved a balance between lightweight design and accuracy. It can be deployed on embedded devices, facilitating practical applications. The model can provide a reference for detecting small target pests in sticky trap images under various multi-source scenarios.

[Objective] Strawberry disease recognition models based on deep neural networks generally assume that the training (source domain) and the test (target domain) datasets are identically and independently distributed. However, in practical applications, due to the influence of illumination, background and strawberry variety, the target domain often exhibits significant domain shift from the source domain. The domain shift result in accuracy decline of the models in target domain. To address this problem, a domain generalization method based on instant whitening and restitution (IWR) was proposed to improve the generalization performance of strawberry disease identification models in this research. [Methods] Samples from different source often exhibit great domain shift due to variations in strawberry varieties, regional climate, and photography methods. Therefore, a dataset was constructed for domain generalization research on strawberry disease using two distinct approaches. The first dataset was acquired using a Nikon D810 camera at multiple strawberry farms in Changfeng county, Anhui province, with a fixed sampling schedule and fixed camera distance. In contrast, the second dataset was an open-source collection, primarily comprising images captured using smartphones in multiple strawberry greenhouses in Korea, with varied and random shooting distances and angles. The IWR module mitigated style variations (e.g., illumination, color) through instance whitening, where features were normalized to reduce domain discrepancies between the datasets. However, such operation was task-ignorant and inevitable removed some task-relevant information, which may be harmful to classification performance of the models. To remedy this, the removed task-relevant features were attempted to recover. Specifically, two modules were designed to extract task-relevant and task-irrelevant feature from the filtered style features, respectively. A dual restitution loss was utilized to constraint the modules' feature correlation between the task and a mutual loss was used to ensure the independence of the features. In addition, a separation optimization strategy was adopted to further enhance the feature separation effect of the two modules. [Results and Discussions] The F₁-Score was adopted as evaluation metrics. A series of ablations studies and comparative experiments were conducted to demonstrate the effectiveness of the proposed IWR. The ablation experiments proved that the IWR could effectively eliminate the style variations between different datasets and separate task-relevant feature from the filtered style features, which could simultaneously enhance model generalization and discrimination capabilities. The recognition accuracy increased when IWR pluged to AlexNet, GoogLeNet, ResNet-18, ResNet-50, MobileNetV2 and MobileNetV3. It demonstrated that the proposed IWR was an effective way to improve the generalization of the models. Compared with other domain generalization methods such as IBNNet, SW and SNR, the generalization performance of the proposed algorithm on test datasets could be improved by 2.63%, 2.35% and 1.14%, respectively. To better understand how IWR works, the intermediate feature maps of ResNet-50 without and with IWR were compared. The visualization result showed that the model with IWR was more robust when the image style changed. These results indicated that the proposed IWR achieves high classification accuracy and boosts the generalization performance of the models. [Conclusions] An instance whitening and restitution module was presented, which aimed to learn generalizable and discriminative feature representations for effective domain generalization. The IWR was a plug-and-play module, it could be inserted into existing convolutional networks for strawberry disease recognition. Style normalization and restitution (SNR) reduced the style information through instance whitening operation and then restitutes the task-relevant discriminative features caused by instance whitening. The introduced dual restitution loss and mutual loss further facilitate the separation of task-relevant and task-irrelevant feature. The schemes powered by IWR achieves the state-of-the-art performance on strawberry disease identification.

[Objective] Sugarcane is an important cash crop, and its health status affects crop yields. However, under natural environmental conditions, the identification of sugarcane leaf diseases is a challenging problem. There are various issues such as disease spots on sugarcane leaves being blocked and interference from lighting, which make it extremely difficult to comprehensively obtain disease information, thus significantly increasing the difficulty of disease identification. Early image recognition algorithms cannot accurately extract disease features and are prone to misjudgment and missed judgment in practical applications. To solve the problem of identifying sugarcane leaf diseases under natural conditions and break through the limitations of traditional methods, a novel identification model, XEffDa was proposed in this research. [Methods] The XEffDa model proposed implemented a series of improvement measures based on the ensemble learning framework, aiming to significantly improve the accuracy of classifying and identifying sugarcane leaf diseases. Firstly, the images in the sugarcane leaf disease dataset under natural conditions were pre-processed. Real-time data augmentation techniques were used to expand the scale of the dataset. Meanwhile, HSV image segmentation and edge-processing techniques were adopted to effectively remove redundant backgrounds and interference factors in the images. Considering that sugarcane leaf disease images were fine-grained images, in order to fully extract the semantic information of the images, the transfer learning strategy was employed. The pre-trained models of EfficientNetB0, Xception, and DenseNet201 were loaded respectively, and with the help of the pre-trained weight parameters based on the ImageNet dataset, the top layers of the models were frozen. The performance of the validation set was monitored through the Bayesian optimization method, and the parameters of the top-layer structure were replaced, thus achieving a good balance between optimizing the number of model parameters and the overall performance. In the top-layer structure, the improved ElasticNet regularization and Dropout layer were integrated. These two mechanisms cooperated with each other to double-suppress overfitting and significantly enhance the generalization ability of the model. During the training process, the MSprop optimizer was selected and combined with the sparse categorical cross - entropy loss function to better adapt to the multi-classification problem of sugarcane disease identification. After each model completed training independently, an exponential weight-allocation strategy was used to organically integrate the prediction features of each model and accurately map them to the final disease categories. To comprehensively evaluate the model performance, the accuracy indicator was continuously monitored, and an early-stopping mechanism was introduced to avoid overfitting and further strengthen the generalization ability of the model. Through the implementation of this series of refined optimization and integration strategies, the XEffDa model for sugarcane leaf diseases was finally successfully constructed. [Results and Discussions] The results of the confusion matrix showed that the XEffDa model performed very evenly across various disease categories, and all indicators achieved excellent results. Especially in the identification of red rot disease, its F₁-Score was as high as 99.09%. This result was not only higher than that of other single models (such as EfficientNetB0 and Xception) but also superior to the combination of EfficientNetB0 and other deep networks (such as DenseNet121 and DenseNet201). This indicated that the XEffDa model significantly improved the ability to extract and classify features of complex pathological images by integrating the advantages of different network architectures. The comparison experiments of different models showed that the recognition accuracy of the XEffDa model reached 97.62%. Compared with the single models of EfficientNetB0 and Xception, as well as the combined models of EfficientNetB0 and other deep networks, the recognition accuracy increased by 9.96, 6.04, 8.09, 4.19, and 1.78 percentage points, respectively. The fusion experiments further showed that the accuracy, precision, recall, and F₁-Score of the network improved by ElasticNet regularization increased by 3.76, 3.76, 3.67, and 3.72 percentage points respectively compared with the backbone network. The results of the maximum-probability scatter plot showed that the proportion of the maximum prediction probability value not lower than 0.5 was as high as 99.4%. [Conclusions] The XEffDa model demonstrated stronger robustness and stability. In the identification task of small sugarcane leaf disease datasets, it showed good generalization ability. This model can provide a powerful reference for the accurate prevention and control of sugarcane crop leaf diseases in practical scenarios, and it has positive significance for promoting the intelligent and precise management of sugarcane production.

[Objective] To address the challenges in detecting tomato leaf diseases and pests, such as complex environments, small goals, low precision, redundant parameters, and high computational complexity, a novel lightweight, high-precision, real-time detection model was proposed called YOLOv10n-YS. This model aims to accurately identify diseases and pests, thereby providing a solid scientific basis for their prevention and management strategies. Methods] The dataset was collected using mobile phones to capture images from multiple angles under natural conditions, ensuring complete and clear leaf images. It included various weather conditions and covered nine types: Early blight, leaf mold, mosaic virus, septoria, spider mites damage, yellow leaf curl virus, late blight, leaf miner disease, and healthy leaves, with all images having a resolution of 640×640 pixels. In the proposed YOLOv10n-YS model, firstly, the C2f in the backbone network was replaced with C2f_RepViTBlock, thereby reducing the computational load and parameter volume and achieving a lightweight design. Secondly, through the introduction of a sliced operation SimAM attention mechanism, the Conv_SWS module was formed, which enhanced the extraction of small target features. Additionally, the DySample lightweight dynamic up sampling module was used to replace the up sampling module in the neck network, concentrating sampling points on target areas and ignoring backgrounds, thereby effectively identifying defects. Finally, the efficient channel attention (ECA) was improved by performing average pooling and max pooling on the input layer to aggregate features and then adding them together, which further enhanced global perspective information and features of different scales. The improved module, known as efficient channel attention with cross-channel interaction (EMCA) attention, was introduced, and the pyramid spatial attention (PSA) in the backbone network was replaced with the EMCA attention mechanism, thereby enhancing the feature extraction capability of the backbone network. [Results and Discussions] After introducing the C2f_RepViTBlock, the model's parameter volume and computational load were reduced by 12.3% and 9.7%, respectively, with mAP@0.5 and F₁-Score each increased by 0.2% and 0.3%. Following the addition of the Conv_SWS and the replacement of the original convolution, mAP@0.5 and F₁-Score were increased by 1.2% and 2%, respectively, indicating that the Conv_SWS module significantly enhanced the model's ability to extract small target features. After the introduction of DySample, mAP@0.5 and F₁-Score were increased by 1.8% and 2.6%, respectively, but with a slight increase in parameter volume and computational load. Finally, the addition of the EMCA attention mechanism further enhanced the feature extraction capability of the backbone network. Through these four improvements, the YOLOv10n-YS model was formed. Compared with the YOLOv10n algorithm, YOLOv10n-YS reduced parameter volume and computational load by 13.8% and 8.5%, respectively, with both mAP@0.5 and F₁-Score increased. These improvements not only reduced algorithm complexity but also enhanced detection accuracy, making it more suitable for industrial real-time detection. The detection accuracy of tomato diseases and pests using the YOLOv10n-YS algorithm was significantly better than that of comparative algorithms, and it had the lowest model parameter volume and computational load. The visualization results of detection by different models showed that the YOLOv10n-YS network could provide technical support for the detection and identification of tomato leaf diseases and pests. To verify the performance and robustness of the YOLOv10n-YS algorithm, comparative experiments were conducted on the public Plant-Village-9 dataset with different algorithms. The results showed that the average detection accuracy of YOLOv10n-YS on the Plant-Village dataset reached 91.1%, significantly higher than other algorithms. [Conclusions] The YOLOv10n-YS algorithm is not only characterized by occupying a small amount of space but also by possessing high recognition accuracy. On the tomato leaf dataset, excellent performance was demonstrated by this algorithm, thereby verifying its broad applicability and showcasing its potential to play an important role in large-scale crop pest and disease detection applications. Deploying the model on drone platforms and utilizing multispectral imaging technology can achieve real-time detection and precise localization of pests and diseases in complex field environments.

[Objective] Agricultural drought has a negative impact on the development of agricultural production and even poses a threat to food security. To reduce disaster losses and ensure stable crop yields, accurately predicting and classifying agricultural drought severity based on the standardized soil moisture index (SSMI) is of significant importance. [Methods] An agricultural drought prediction model, GCN-BiGRU-STMHSA was proposed, which integrated a graph convolutional network (GCN), a bidirectional gated recurrent unit (BiGRU), and a multi-head self-attention (MHSA) mechanism, based on remote sensing data. In terms of model design, the proposed method first employed GCN to fully capture the spatial correlations among different meteorological stations. By utilizing GCN, a spatial graph structure based on meteorological stations was constructed, enabling the extraction and modeling of spatial dependencies between stations. Additionally, a spatial multi-head self-attention mechanism (S-MHSA) was introduced to further enhance the model's ability to capture spatial features. For temporal modeling, BiGRU was utilized as the time-series feature extraction module. BiGRU considers both forward and backward dependencies in time-series data, enabling a more comprehensive understanding of the temporal dynamics of agricultural drought. Meanwhile, a temporal multi-head self-attention mechanism (T-MHSA) was incorporated to enhance the model's capability to learn long-term temporal dependencies and improve prediction stability across different time scales. Finally, the model employed a fully connected layer to perform regression prediction of the SSMI. Based on the classification criteria for agricultural drought severity levels, the predicted SSMI values were mapped to the corresponding drought severity categories, achieving precise agricultural drought classification. To validate the effectiveness of the proposed model, the global land data assimilation system (GLDAS_2.1) dataset and conducted modeling and experiments was utilized on five representative meteorological stations in the North China Plain (Xinyang, Gushi, Fuyang, Huoqiu, and Dingyuan). Additionally, the proposed model was compared with multiple deep learning models, including GRU, LSTM, and Transformer, to comprehensively evaluate its performance in agricultural drought prediction tasks. The experimental design covered different forecasting horizons to analyze the model's generalization capability in both short-term and long-term predictions, thereby providing a more reliable early warning system for agricultural drought. [Results and Discussions] Experimental results demonstrated that the proposed GCN-BiGRU-STMHSA model outperforms baseline models in both SSMI prediction and agricultural drought classification tasks. Specifically, across the five study stations, the model achieved significantly lower mean absolute error (MAE) and root mean squared error (RMSE), while attaining higher coefficient of determination ( R²), classification accuracy (ACC), and F₁-Score ( F₁). Notably, at the Gushi station, the model exhibited the best performance in predicting SSMI 10 days ahead, achieving an MAE of 0.053, a RMSE of 0.071, a R² of 0.880, an ACC of 0.925, and a F₁ of 0.924. Additionally, the model's generalization capability was investigated under different forecasting horizons (7, 14, 21, and 28 days). Results indicated that the model achieved the highest accuracy in short-term predictions (7 days). Although errors increase slightly as the prediction horizon extends, the model maintained high classification accuracy even for long-term predictions (up to 28 days). This highlighted the model's robustness and effectiveness in agricultural drought prediction over varying time scales. [Conclusions] The proposed model achieves superior accuracy and generalization capability in agricultural drought prediction and classification. By effectively integrating spatial graph modeling, temporal sequence feature extraction, and self-attention mechanisms, the model outperforms conventional deep learning approaches in both short-term and long-term forecasting tasks. Its strong performance provides accurate drought early warnings, assisting agricultural management authorities in formulating efficient water resource management strategies and optimizing irrigation plans. This contributes to safeguarding agricultural production and mitigating the potential adverse effects of agricultural drought.

[Significance] Developing new-quality productivity is of great significance for promoting high-quality development of animal husbandry. However, there is currently limited research on new-quality productivity in animal husbandry, and there is a lack of in-depth analysis on its connotation, characteristics, constraints, and promotion path. [Progress] This article conducts a systematic study on the high-quality development of animal husbandry productivity driven by artificial intelligence. The new-quality productivity of animal husbandry is led by cutting-edge technological innovations such as biotechnology, information technology, and green technology, with digitalization, greening, and ecologicalization as the direction of industrial upgrading. Its basic connotation is manifested as higher quality workers, more advanced labor materials, and a wider range of labor objects. Compared with traditional productivity, the new-quality productivity of animal husbandry is an advanced productivity guided by technological innovation, new development concepts, and centered on the improvement of total factor productivity. It has significant characteristics of high production efficiency, good industrial benefits, and strong sustainable development capabilities. China's new-quality productivity in animal husbandry has a good foundation for development, but it also faces constraints such as insufficient innovation in animal husbandry breeding technology, weak core competitiveness, low mechanization rate of animal husbandry, weak independent research and development capabilities of intelligent equipment, urgent demand for "machine replacement", shortcomings in the quantity and quality of animal husbandry talents, low degree of scale of animal husbandry, and limited level of intelligent management. Artificial intelligence in animal husbandry can be widely used in environmental control, precision feeding, health monitoring and disease prevention and control, supply chain optimization and other fields. Artificial intelligence, through revolutionary breakthroughs in animal husbandry technology represented by digital technology, innovative allocation of productivity factors in animal husbandry linked by data elements, and innovative allocation of productivity factors in animal husbandry adapted to the digital economy, has given birth to new-quality productivity in animal husbandry and empowered the high-quality development of animal husbandry. [Conclusions and Prospects] This article proposes a path to promote the development of new-quality productivity in animal husbandry by improving the institutional mechanism of artificial intelligence to promote the development of modern animal husbandry industry, strengthening the application of artificial intelligence in animal husbandry technology innovation and promotion, and improving the management level of artificial intelligence in the entire industry chain of animal husbandry.