Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system.

In recent years, traditional farming methods have been increasingly replaced by more modern, intelligent farming techniques. This shift towards information and intelligence in farming is becoming a trend. When they are bitten by dinoflagellates, cows display stress behaviors, including tail wagging, head tossing, leg kicking, ear flapping, and skin fluttering. The study of cow protective behavior can indirectly reveal the health status of cows and their living patterns under different environmental conditions, allowing for the evaluation of the breeding environment and animal welfare status. In this study, we generated key point feature marker information using the DeepLabCut target detection algorithm and constructed the spatial relationship of cow feature marker points to detect the cow’s protective behavior based on the change in key elements of the cow’s head swinging and walking performance. The algorithm can detect the protective behavior of cows, with the detection accuracy reaching the level of manual detection. The next step in the research focuses on analyzing the differences in protective behaviors of cows in different environments, which can help in cow breed selection. It is an important guide for diagnosing the health status of cows and improving milk production in a practical setting.

Accurate monitoring of cattle behavioral time budgets is crucial for early disease detection and welfare assessment. Changes in durations of standing, lying, and eating are known to be early indicators of health issues such as lameness and metabolic disorders. To enable low-cost, non-invasive, and real-time monitoring, this study proposes a lightweight cattle behavior recognition method based on an improved YOLO11n architecture. The model enhances multi-scale feature integration through a generalized efficient layer aggregation network (GELAN), improves feature extraction via a multidimensional collaborative attention (MCA) mechanism, and achieves efficient cross-scale fusion using a bidirectional feature pyramid network (BiFPN). Depthwise separable convolution (DWConv) is incorporated to reduce computational load. Experimental results demonstrate high recognition accuracy, with mAP@0.5 values of 91.2%, 91.0%, and 93.9% for standing, lying, and eating, respectively. The model was subsequently compressed using a Layer-adaptive Magnitude-based Pruning (LAMP) algorithm, resulting in a final model of only 1.06 × 10⁶ parameters, a computational cost of 6.3 GFLOPS, and a weight size of 2.4 MB, while retaining 90.7% mAP@0.5. This highly efficient system is suitable for deployment on resource-constrained edge devices, providing a practical tool for continuous cattle monitoring. It offers a viable pathway for farmers to adopt precision livestock farming practices, facilitating early health intervention and promoting animal welfare.

Protective behavior in dairy cattle is one of the important potential indicators of their health and welfare status, and the precise detection of this behavior is of great significance for improving pasture management. However, existing methods face challenges, including capturing rapid motions, excessive background interference, and sample imbalance in complex agricultural environments. In response to these challenges, we proposed a Multi-Stage Attention SlowFast (MSA-SlowFast) model based on the improved SlowFast network to explore the model’s ability to distinguish between normal and protective behavior of dairy cattle. It achieves performance improvement through three core modules: the Multi-Path Balanced Head (MPBHead) for alleviating category imbalance, the Spatio-Temporal Convolutional Block Attention Module (ST-CBAM) for enhancing key feature extraction, and the 7 (BAF) for promoting multi-path feature complementarity. Additionally, we proposed novel timing-aware oversampling methods and dynamic loss adjustment mechanisms to further improve the detection performance of minority-class protective behaviors. Finally, a spatio-temporal-oriented dairy cattle protective behaviors dataset is constructed. Experimental results demonstrate that the proposed MSA-SlowFast model achieves 79.41% mAP, surpassing the standard SlowFast (70.58%) and Slow-only (68.21%). Further validation shows that the model exhibits high detection confidence in four specific actions labeled as protective behavior: 0.97 for tail swaying, 0.90 for head shaking, 0.92 for ear flapping, and 0.90 for leg kicking. These preliminary results show that the method proposed in this study has certain feasibility and reference value for the detection of protective behavior of dairy cattle under our constructed dataset.