Potato is the fourth largest food crop in the world. Disease is an important factor restricting potato yield. Disease detection based on deep learning has strong advantages in network structure, training speed, detection accuracy, and other aspects. This article took potato leaf diseases (early blight and viral disease) as the research objects, collected disease images to construct a disease dataset, and expanded the dataset through data augmentation methods to improve the quantity and diversity of the dataset. Four classic deep learning networks (VGG16, MobilenetV1, Resnet50, and Vit) were used to train the dataset, and the VGG16 network had the highest accuracy of 97.26%; VGG16 was chosen as the basic research network. A new, improved algorithm, VGG16S, was proposed to solve the problem of large network parameters by using three improvement methods: changing the network structure of the VGG16 network from “convolutional layer + flattening layer + fully connected layer” to “convolutional layer + global average pooling”, integrating CBAM attention mechanism, and introducing Leaky ReLU activation function for learning and training. The improved VGG16S network has a parameter size of 15 M (1/10 of VGG16), and the recognition accuracy of the test set is 97.87%. This article used response surface analysis to optimize hyperparameters, and the test results indicated that VGG16S, after hyperparameter tuning, had further improved its diagnostic performance. At last, this article completed ablation experiments and public dataset testing. The research results will provide a theoretical basis for the timely adoption of corresponding prevention and control measures, improving the yield and quality of potatoes and increasing economic benefits.

The daily behaviors of dairy cows, including standing, drinking, eating, and lying down, are closely associated with their physical health. Efficient and accurate recognition of dairy cow behaviors is crucial for timely monitoring of their health status and enhancing the economic efficiency of farms. To address the challenges posed by complex scenarios and significant variations in target scales in dairy cow behavior recognition within group farming environments, this study proposes an enhanced recognition method based on YOLOv5. Four Shuffle Attention (SA) modules are integrated into the upsampling and downsampling processes of the YOLOv5 model’s neck network to enhance deep feature extraction of small-scale cow targets and focus on feature information, while maintaining network complexity and real-time performance. The C3 module of the model was enhanced by incorporating Deformable convolution (DCNv3), which improves the accuracy of cow behavior characteristic identification. Finally, the original detection head was replaced with a Dynamic Detection Head (DyHead) to improve the efficiency and accuracy of cow behavior detection across different scales in complex environments. An experimental dataset comprising complex backgrounds, multiple behavior categories, and multi-scale targets was constructed for comprehensive validation. The experimental results demonstrate that the improved YOLOv5 model achieved a mean Average Precision (mAP) of 97.7%, representing a 3.7% improvement over the original YOLOv5 model. Moreover, it outperformed comparison models, including YOLOv4, YOLOv3, and Faster R-CNN, in complex background scenarios, multi-scale behavior detection, and behavior type discrimination. Ablation experiments further validate the effectiveness of the SA, DCNv3, and DyHead modules. The research findings offer a valuable reference for real-time monitoring of cow behavior in complex environments throughout the day.

Greenhouses, as important parts of facility agriculture, can reduce the restrictions on agricultural production imposed by the natural environment and make rational and efficient use of production resources. We conducted long-term, continuous testing of temperature, humidity, light intensity, and CO₂ concentration parameters in a heliostat greenhouse in the central and western parts of the Inner Mongolia Autonomous Region, a cold and arid region of northern China. A large amount of data was processed by statistical observation, simulation analysis, and 3D reconstruction to obtain the overall distribution, variation pattern, and mathematical model of the regional greenhouse environment in time and space. The results show that the temperature, humidity, light intensity, and CO₂ concentration in the greenhouse have significant daily variation patterns, that there are strong coupling relationships between light intensity–CO₂ concentration–time and indoor temperature–light intensity–CO₂ concentration, that the coefficients of determination (R²) of the mathematical models are 0.88 and 0.89, and that the standard errors (RMSE) are 49.67 ppm and 45.30 ppm, respectively. The environmental parameters were fitted with high accuracy in order to provide scientific data for the cultivation of heliostats in the region.