YOLO-CRD:A Lightweight Model for the Detection of Rice Diseases in Natural Environments

Rice diseases can adversely affect both the yield and quality of rice crops,leading to the increased use of pesticides and environmental pollution.Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance.Deep learning-based disease identification technologies have shown promise in automatically discerning disease types.However,effectively extracting early disease features in natural environments remains a challenging problem.To address this issue,this study proposes the YOLO-CRD method.This research selected images of common rice diseases,primarily bakanae disease,bacterial brown spot,leaf rice fever,and dry tip nematode disease,from Tianjin Xiaozhan.The proposed YOLO-CRD model enhanced the YOLOv5s network architecture with a Convolutional Channel Attention Module,Spatial Pyramid Pooling Cross-Stage Partial Channel module,and Ghost module.The former module improves attention across image channels and spatial dimensions,the middle module enhances model generalization,and the latter module reduces model size.To validate the feasibility and robustness of this method,the detection model achieved the following metrics on the test set:mean average precision of 90.2%,accuracy of 90.4%,F1-score of 88.0,and GFLOPS of 18.4.for the specific diseases,the mean average precision scores were 85.8%for bakanae disease,93.5%for bacterial brown spot,94%for leaf rice fever,and 87.4%for dry tip nematode disease.Case studies and comparative analyses verified the effectiveness and superiority of the proposed method.These researchfind-ings can be applied to rice disease detection,laying the groundwork for the development of automated rice disease detection equipment.

Underwater Sea Cucumber Target Detection Based on Edge-Enhanced Scaling YOLOv4

Sea cucumber detection is widely recognized as the key to automatic culture.The underwater light environment is complex and easily obscured by mud,sand,reefs,and other underwater organisms.To date,research on sea cucumber detection has mostly concentrated on the distinction between prospective objects and the background.However,the key to proper distinction is the effective extraction of sea cucumber feature information.In this study,the edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4)was proposed for sea cucumber detection.By emphasizing the target features in a way that reduced the impact of different hues and brightness values underwater on the misjudgment of sea cucumbers,a bidirectional cascade network(BDCN)was used to extract the overall edge greyscale image in the image and add up the original RGB image as the detected input.Meanwhile,the YOLOv4 model for backbone detection is scaled,and the number of parameters is reduced to 48%of the original number of parameters.Validation results of 783images indicated that the detection precision of positive sea cucumber samples reached 0.941.This improvement reflects that the algorithm is more effective to improve the edge feature information of the target.It thus contributes to the automatic multi-objective detection of underwater sea cucumbers.

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.

Accurate detection method of pig's temperature based on non-point source thermal infrared image

Body temperature measurement is a very important task in the sow breeding process.The authors used an infrared camera to detect the temperature of the body surface of the sows,relying on calculating the average of the infrared image temperature in the ear root region.Based on the grayscale value of the target image of the infrared image and the corresponding temperature value of 180 infrared images,a G-T(Gray-Temperature)model was established by linear least squares method,which achieved temperature inversion of each pixel of the target pig.For the different growth stages and different breeds of sows,the R-square of the all established models is greater than 0.95.The average relative error of the model inversion of the body temperature was only 0.076977%.This means that the body temperature of the sows could be detected without relying on the software.Based on the G-T model,the authors design a kind of sow's ear root recognition and body surface temperature detection algorithm for different sow population scenarios.

Automatic detection of sow estrus using a lightweight real-time detector and thermal images

Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.

A potent PGK1 antagonist reveals PGK1 regulates the production of IL-1βand IL-6

Glycolytic metabolism enzymes have been implicated in the immunometabolism field through changes in metabolic status. PGK1 is a catalytic enzyme in the glycolytic pathway. Here, we set up a high-throughput screen platform to identify PGK1 inhibitors. DC-PGKI is an ATPcompetitive inhibitor of PGK1 with an affinity of Kd= 99.08 nmol/L. DC-PGKI stabilizes PGK1in vitro and in vivo, and suppresses both glycolytic activity and the kinase function of PGK1. In addition,DC-PGKI unveils that PGK1 regulates production of IL-1β and IL-6 in LPS-stimulated macrophages.Mechanistically, inhibition of PGK1 with DC-PGKI results in NRF2(nuclear factor-erythroid factor 2-related factor 2, NFE2L2) accumulation, then NRF2 translocates to the nucleus and binds to the proximity region of Il-1β and Il-6 genes, and inhibits LPS-induced expression of these genes. DC-PGKI ameliorates colitis in the dextran sulfate sodium(DSS)-induced colitis mouse model. These data support PGK1 as a regulator of macrophages and suggest potential utility of PGK1 inhibitors in the treatment of inflammatory bowel disease.

Study on body temperature detection of pig based on infrared technology: A review

Body temperature is an important physiological indicator in the whole process of pig breeding.Temperature measurement is also an effective means to assist in disease diagnosis and pig health monitoring.In the conventional method of measuring body temperature,a mercury column is used to obtain the rectal temperature.The operation of thismethod is complicated and requires a large amount of labor.This kind of temperature measurement method is contact and canmake the pig stressed,which is disadvantageous for the healthy growth of pigs.Therefore,rectal temperaturemeasurement no longer meets the needs of the large-scale pig industry in China's welfare agriculture.In recent years,the emerging pig body temperature detection technologies are electronic temperaturemeasurement technology,infrared temperature measurement technology and so on.Infrared temperature measurement technology has been the main means of measuring the temperature of pig body surface with its advantages of non-contact,long distance and real-time.At present,infrared temperature measurement technology and infrared image processing technology used in pig breeding are still in the exploration stage.Nowadays,the infrared temperature measurement equipment based on point-by-point analysis represented by infrared thermometer and temperature measurement equipment based on full-field analysis represented by infrared thermal imager have been applied to pig breeding industry.These types of temperaturemeasurement are more in line with the needs of the pig breeding industry to transform and upgrade to the automation,in line with the development concept of welfare farming and smart agriculture,and its development prospects are very impressive.