Mastitis is a major disease affecting dairy cow health and milk production. This study established an integrated machine learning (ML) model combining herd- and individual-level data to achieve efficient and balanced prediction of clinical mastitis. Data were collected from 5284 lactating Holstein cows on two farms in southern and northern China. Five feature processing methods—recursive feature elimination (RFE), contrastive learning (CL), slopes and intercept, milk-conductivity ratio, and differences—were evaluated with four ML algorithms: Support vector machine (SVM), random forest (RF), XGBoost, and backpropagation neural network (BPNN). Among them, the XGBoost model with the milk-conductivity ratio feature achieved the best performance, with a sensitivity of 0.81 and specificity of 0.75. To further address the imbalance between sensitivity and specificity, collaborative filtering (CF) was introduced into the XGBoost model to incorporate both herd and individual cow information. The resulting XGBoost–CF model improved sensitivity to 0.83 and specificity to 0.87, enhancing the model’s ability to identify both healthy and diseased cows. This integrated ML–CF framework provides an effective strategy for early mastitis prediction, offering practical support for intelligent dairy herd management and precision livestock farming.

The development of intelligent decision-making technologies for agricultural machinery has been a critical research focus over the past decade, as they serve as the key enabler for autonomous operation and large-scale cooperative tasks in modern farming. The primary goal of previous studies was to improve operational efficiency, adaptability, and coordination of agricultural equipment under dynamic and uncertain field conditions. While many existing works concentrated on specific stages of the integrated “perception–control–execution” process, comprehensive reviews of decision-making architectures and their systematic frameworks remained limited. Against this background, the present study aimed to systematically examine the state of research on intelligent decision-making in agricultural machinery, with particular attention to perception modeling, decision-making strategies, and adaptive control mechanisms. To achieve this objective, the study first adopted a comparative approach to survey multi-source heterogeneous information perception techniques. The methods analyzed included state estimation models, semantic fusion strategies, and scene-adaptive configurations of sensors and equipment. Special emphasis was placed on multi-modal fusion structures that integrated environmental information with operational states, enabling robust perception under varying task requirements. This phase of the review also considered the challenges of processing noisy data, ensuring sensor reliability, and establishing consistent standards for agricultural environments that are inherently unstructured and variable. Subsequently, the review examined three distinct categories of intelligent decision-making generation methods: rule-driven approaches, optimization-driven models, and learning-driven algorithms. Each category was assessed according to its applicable conditions, computational requirements, and adaptability to real-world agricultural environments. Rule-based methods emphasized interpretability and transparency but lacked flexibility when conditions deviated from predefined patterns. Optimization-driven methods, such as task scheduling and path planning algorithms, were particularly effective for resource allocation and route efficiency, though they often demanded extensive computational power. Learning-driven methods, most notably reinforcement learning and deep learning approaches, offered superior adaptability and the capacity to evolve through experience, but they faced limitations in terms of training data requirements, stability, and deployment feasibility in real time. The results of this comprehensive review demonstrated that multi-modal perception modeling successfully improved the robustness of environmental sensing, particularly under diverse and uncertain field conditions. Rule-driven decision-making approaches provided reliability and interpretability but exhibited limited flexibility in rapidly changing environments. Optimization-driven strategies showed strong performance in path planning and task allocation but required significant computational resources. Learning-driven methods, particularly those based on deep reinforcement learning, achieved superior adaptability and self-improvement, though their real-time applicability remained constrained by model complexity and data requirements. In terms of adaptive control, experimental studies highlighted the effectiveness of feedback-based real-time control mechanisms for adjusting machinery behaviors, thereby ensuring stability and safety during operation. Additionally, system-level intelligent decision-making frameworks were found to enhance multi-machine collaboration, farm management efficiency, and integration with intelligent management platforms. Evaluations of practical applications in crop monitoring, path optimization, collaborative scheduling, and smart farm platforms indicated promising results in improving both productivity and resource efficiency. In conclusion, intelligent decision-making technology for agricultural machinery is now positioned as a core driver of smart agriculture. However, challenges persist in perception robustness, adaptability of decision algorithms, and the maturity of large-scale applications. Current findings suggest that future progress depends on strengthening multi-modal and multi-factor coupling mechanisms, developing large-model-driven decision-making frameworks, and incorporating digital twin simulations for real-time optimization and validation. Moreover, progress in this field will require closer integration of artificial intelligence with domain-specific agricultural expertise, as well as cross-disciplinary cooperation among agronomy, computer science, and engineering. These advances are expected to unify agricultural efficiency, economic profitability, and ecological sustainability, thereby providing a strategic reference for the future development of smart agriculture and intelligent agricultural equipment.