Green pepper fruits counting based on improved DeepSort and optimized Yolov5s.

Introduction: Green pepper yield estimation is crucial for establishing harvest and storage strategies. Method: This paper proposes an automatic counting method for green pepper fruits based on object detection and multi-object tracking algorithm. Green pepper fruits have colors similar to leaves and are often occluded by each other, posing challenges for detection. Based on the YOLOv5s, the CS_YOLOv5s model is specifically designed for green pepper fruit detection. In the CS_YOLOv5s model, a Slim-Nick combined with GSConv structure is utilized in the Neck to reduce model parameters while enhancing detection speed. Additionally, the CBAM attention mechanism is integrated into the Neck to enhance the feature perception of green peppers at various locations and enhance the feature extraction capabilities of the model. Result: According to the test results, the CS_YOLOv5s model of mAP, Precision and Recall, and Detection time of a single image are 98.96%, 95%, 97.3%, and 6.3 ms respectively. Compared to the YOLOv5s model, the Detection time of a single image is reduced by 34.4%, while Recall and mAP values are improved. Additionally, for green pepper fruit tracking, this paper combines appearance matching algorithms and track optimization algorithms from SportsTrack to optimize the DeepSort algorithm. Considering three different scenarios of tracking, the MOTA and MOTP are stable, but the ID switch is reduced by 29.41%. Based on the CS_YOLOv5s model, the counting performance before and after DeepSort optimization is compared. For green pepper counting in videos, the optimized DeepSort algorithm achieves ACP (Average Counting Precision), MAE (Mean Absolute Error), and RMSE (Root Mean Squared Error) values of 95.33%, 3.33, and 3.74, respectively. Compared to the original algorithm, ACP increases by 7.2%, while MAE and RMSE decrease by 6.67 and 6.94, respectively. Additionally, Based on the optimized DeepSort, the fruit counting results using YOLOv5s model and CS_YOLOv5s model were compared, and the results show that using the better object detector CS_YOLOv5s has better counting accuracy and robustness.

Field cabbage detection and positioning system based on improved YOLOv8n.

BACKGROUND: Pesticide efficacy directly affects crop yield and quality, making targeted spraying a more environmentally friendly and effective method of pesticide application. Common targeted cabbage spraying methods often involve object detection networks. However, complex natural and lighting conditions pose challenges in the accurate detection and positioning of cabbage. RESULTS: In this study, a cabbage detection algorithm based on the YOLOv8n neural network (YOLOv8-cabbage) combined with a positioning system constructed using a Realsense depth camera is proposed. Initially, four of the currently available high-performance object detection models were compared, and YOLOv8n was selected as the transfer learning model for field cabbage detection. Data augmentation and expansion methods were applied to extensively train the model, a large kernel convolution method was proposed to improve the bottleneck section, the Swin transformer module was combined with the convolutional neural network (CNN) to expand the perceptual field of feature extraction and improve edge detection effectiveness, and a nonlocal attention mechanism was added to enhance feature extraction. Ablation experiments were conducted on the same dataset under the same experimental conditions, and the improved model increased the mean average precision (mAP) from 88.8% to 93.9%. Subsequently, depth maps and colour maps were aligned pixelwise to obtain the three-dimensional coordinates of the cabbages via coordinate system conversion. The positioning error of the three-dimensional coordinate cabbage identification and positioning system was (11.2 mm, 10.225 mm, 25.3 mm), which meets the usage requirements. CONCLUSIONS: We have achieved accurate cabbage positioning. The object detection system proposed here can detect cabbage in real time in complex field environments, providing technical support for targeted spraying applications and positioning.

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

Evaluation of stromal myofibroblasts in oral submucous fibrosis and its malignant transformation: An immunohistochemical study.

BACKGROUND: Oral submucous fibrosis (OSF) is a precancerous lesion, with oral squamous cell carcinoma (OSCC) being the most prevalent malignancy affecting the oral mucosa. The malignant transformation of OSF into OSCC is estimated to occur in 7-13% of cases. Myofibroblasts (MFs) play pivotal roles in both physiological and pathological processes, such as wound healing and tumorigenesis, respectively. This study aimed to explore the involvement of MFs in the progression of OSF and its malignant transformation. MATERIALS AND METHODS: In total, 94 formalin-fixed paraffin-embedded tissue blocks were collected, including normal oral mucosa (NOM; n = 10), early-moderate OSF (EMOSF; n = 29), advanced OSF (AOSF; n = 29), paracancerous OSF (POSF; n = 21), and OSCC (n = 5) samples. Alpha-smooth muscle actin was used for the immunohistochemical identification of MFs. RESULTS: NOM exhibited infrequent expression of MFs. A higher staining index of MFs was found in AOSF, followed by EMOSF and NOM. Additionally, a significant increase in the staining index of MFs was found from EMOSF to POSF and OSCC. The staining index of MFs in NOM, EMOSF, AOSF, POSF, and OSCC was 0.14 ± 0.2, 1.69 ± 1.4, 2.47 ± 1.2, 3.57 ± 2.6, and 8.86 ± 1.4, respectively. All results were statistically significant (P < 0.05). CONCLUSIONS: The expression of MFs exhibited a gradual increase as the disease progressed from mild to malignant transformation, indicating the contributory role of MFs in the fibrogenesis and potential tumorigenesis associated with OSF.

Research on a Visual/Ultra-Wideband Tightly Coupled Fusion Localization Algorithm.

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a vision SLAM/UWB tightly coupled localization method and designs a UWB non-line-of-sight error identification method using the displacement increment of the visual odometer. It utilizes the displacement increment of visual output and UWB ranging information as measurement values and applies the extended Kalman filtering algorithm for data fusion. This study utilized the constructed experimental platform to collect images and ultra-wideband ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that the algorithm outperforms individual UWB or loosely coupled combination positioning methods in terms of positioning accuracy. It effectively eliminates non-line-of-sight errors in UWB, improving the accuracy and stability of the combined positioning system.

A Novel Peptide COX52-69 Inhibits High Glucose-induced Insulin Secretion by Modulating BK Channel Activity.

BACKGROUND: Excessive insulin is the leading cause of metabolic syndromes besides hyperinsulinemia. Insulin-lowering therapeutic peptides have been poorly studied and warrant urgent attention. OBJECTIVE: The main purpose of this study, was to introduce a novel peptide COX52-69 that was initially isolated from the porcine small intestine and possessed the ability to inhibit insulin secretion under high-glucose conditions by modulating large conductance Ca2+-activated K+ channels (BK channels) activity. METHODS AND RESULTS: Enzyme-linked immunosorbent assay results indicate that COX52-69 supressed insulin release induced by high glucose levels in pancreatic islets and animal models. Furthermore, electrophysiological data demonstrated that COX52-69 can increase BK channel currents and hyperpolarize cell membranes. Thus, cell excitability decreased, corresponding to a reduction in insulin secretion. CONCLUSION: Our study provides a novel approach to modulate high glucose-stimulated insulin secretion in patients with hyperinsulinemia.

An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study.

The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.

Amygdala Metabotropic Glutamate Receptor 1 Influences Synaptic Transmission to Participate in Fentanyl-Induced Hyperalgesia in Rats.

The underlying mechanisms of opioid-induced hyperalgesia (OIH) remain unclear. Herein, we found that the protein expression of metabotropic glutamate receptor 1 (mGluR1) was significantly increased in the right but not in the left laterocapsular division of central nucleus of the amygdala (CeLC) in OIH rats. In CeLC neurons, the frequency and the amplitude of mini-excitatory postsynaptic currents (mEPSCs) were significantly increased in fentanyl group which were decreased by acute application of a mGluR1 antagonist, A841720. Finally, the behavioral hypersensitivity could be reversed by A841720 microinjection into the right CeLC. These results show that the right CeLC mGluR1 is an important factor associated with OIH that enhances synaptic transmission and could be a potential drug target to alleviate fentanyl-induced hyperalgesia.