Timely and accurate identification of tea tree pests is critical for effective tea tree pest control. We collected image data sets of eight common tea tree pests to accurately represent the true appearance of various aspects of tea tree pests. The dataset contains 782 images, each containing 1~5 different pest species randomly distributed. Based on this dataset, a tea garden pest detection and recognition model was designed using the Yolov7-tiny network target detection algorithm, which incorporates deformable convolution, the Biformer dynamic attention mechanism, a non-maximal suppression algorithm module, and a new implicit decoupling head. Ablation experiments were conducted to compare the performance of the models, and the new model achieved an average accuracy of 93.23%. To ensure the validity of the model, it was compared to seven common detection models, including Efficientdet, Faster Rcnn, Retinanet, DetNet, Yolov5s, YoloR, and Yolov6. Additionally, feature visualization of the images was performed. The results demonstrated that the Improved Yolov7-tiny model developed was able to better capture the characteristics of tea tree pests. The pest detection model proposed has promising application prospects and has the potential to reduce the time and economic cost of pest control in tea plantations.

Accurately detecting and identifying granary pests is important in effectively controlling damage to a granary, ensuring food security scientifically and efficiently. In this paper, multi-scale images of seven common granary pests were collected. The dataset had 5231 images acquired with DSLR-shot, microscope, cell phone and online crawler. Each image contains different species of granary pests in a different background. In this paper, we designed a multi-scale granary pest recognition model, using the YOLOv5 (You Look Only Once version 5) object detection algorithm incorporating bidirectional feature pyramid network (BiFPN) with distance intersection over union, non-maximum suppression (DIOU_NMS) and efficient channel attention (ECA) modules. In addition, we compared the performance of the different models established with Efficientdet, Faster rcnn, Retinanet, SSD, YOLOx, YOLOv3, YOLOv4 and YOLOv5s, and we designed improved YOLOv5s on this dataset. The results show that the average accuracy of the model we designed for seven common pests reached 98.2%, which is the most accurate model among those identified in this paper. For further detecting the robustness of the proposed model, ablation analysis was conducted. Furthermore, the results show that the average accuracy of models established using the YOLOv5s network model combined with the attention mechanism was 96.9%. When replacing the model of PANet with BiFPN, the average accuracy reached 97.2%. At the same time, feature visualization was analyzed. The results show that the proposed model is good for capturing features of pests. The results of the model have good practical significance for the recognition of multi-scale granary pests.

Mildew infestation is a significant cause of loss during grain storage. The growth and metabolism of mildew leads to changes in gas composition and temperature within granaries. Recent advances in sensor technology and machine learning enable the prediction of grain mildew during storage. Current research primarily focuses on predicting mildew occurrence or grading using simple machine learning methods, without in-depth exploration of the time series characteristics of mildew process data. A monitoring device was designed and developed to capture high-quality microenvironment parameters and image data during a simulated mildew process experiment. Using the “Yongyou 15” rice varieties from Zhejiang Province, five simulation experiments were conducted under varying temperature and humidity conditions between January and May 2023. Mildew grades were defined through manual analysis to construct a multimodal dataset for the rice mildew process. This study proposes a combined model (CNN–LSTM–A) that integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the mildew grade of stored rice. The proposed model was compared with LSTM, CNN–LSTM, and LSTM–Attention models. The results indicate that the proposed model outperforms the others, achieving a prediction accuracy of 98%. The model demonstrates superior accuracy and more stable performance. The generalization performance of the prediction model was evaluated using four experimental datasets with varying storage temperature and humidity conditions. The results show that the model achieves optimal prediction stability when the training set contains similar storage temperatures, with prediction accuracy exceeding 99.8%. This indicates that the model can effectively predict the mildew grades in rice under varying environmental conditions, demonstrating significant potential for grain mildew prediction and early warning systems.

This study examined spatial and temporal thermal performance and energy consumption. The temperature distribution in the running period was monitored in test rooms with integrated electric- and hot water-heated floors. The short- and long-term energy consumption of the two heating systems were recorded and compared. The results indicated that the integrated electric heating system generated higher temperatures for indoor air and on the exterior surface of the wooden floor than the hot water heating system; meanwhile, the difference in the mean temperatures of the exterior and rear surfaces of the electric-heated floor was 2.44 °C, while that of the hot water-heated test room was 13.25 °C. The efficient structure of the integrated electric heating system saved 22.97% energy compared to the hot water system after short-term (7 h) charging and reaching a dynamic balance, and it efficiently increased the energy utilization rate to 11.81%. After long-term charging, the daily energy consumption of the integrated electric heating system consumed much less energy than the hot water system every month. The integrated electric heating system saved 62.55% and 34.30% of energy in May and January, respectively, and consumed less than half of the energy the hot water system consumed in the less cold months. Therefore, a high-efficiency and energy-saving integrated electric-heated floor could be a potential indoor heating solution.

In this study, we proposed a fast and accurate PatchMixer-based framework (Patch-TS). After the data were processed, which included missing values and normalization, the environmental factors were selected via the Pearson correlation coefficient method. Then, the newly developed sap flow prediction model was trained. The resulting data demonstrated that the coefficient of determination (R²), mean squared error (MSE), and mean absolute error (MAE) are 0.921, 0.00824, and 0.0497, respectively. The R² of Patch-TS further improved to 0.929 after 7 factors were extracted via the Pearson correlation method. Furthermore, we comparatively analyse the mitigating effects of RevIn (Reversible Instance Normalization) and Dish-TS on data drift. In addition to the predictive performance of the models under different prediction windows, Patch-TS outperforms the other models. The results demonstrate that the model developed in this paper is an effective tool for accurately predicting sap flow, which is a valuable contribution to the practical management of trees and forests.

The accurate identification of tea tree pests is crucial for tea production, as it directly impacts yield and quality. In natural tea garden environments, identifying pests is challenging due to their small size, similarity in color to tea trees, and complex backgrounds. To address this issue, we propose TTPRNet, a multi-scale recognition model designed for real tea garden environments. TTPRNet introduces the ConvNext architecture into the backbone network to enhance the global feature learning capabilities and reduce the parameters, and it incorporates the coordinate attention mechanism into the feature output layer to improve the representation ability for different scales. Additionally, GSConv is employed in the neck network to reduce redundant information and enhance the effectiveness of the attention modules. The NWD loss function is used to focus on the similarity between multi-scale pests, improving recognition accuracy. The results show that TTPRNet achieves a recall of 91% and a mAP of 92.8%, representing 7.1% and 4% improvements over the original model, respectively. TTPRNet outperforms existing object detection models in recall, mAP, and recognition speed, meeting real-time requirements. Furthermore, the model integrates a counting function, enabling precise tallying of pest numbers and types and thus offering practical solutions for accurate identification in complex field conditions.