Video processing plays an important role in the intelligent monitoring and management system of agricultural information. Video shot boundary detection is the basic symmetry step underlying video processing techniques. According to the current shot boundary detection algorithm, the feature changes between gradual transition frames are difficult to detect, and the misdetection situation is caused by ignoring the attention of the target feature during the feature extraction. A novel symmetry multi-step comparative scheme of shot boundary detection algorithm based on global features and target features is proposed. First, the RGB color histogram features of the video frame are extracted. Second, foreground object detection for the video frames is performed using the Gaussian Mixture Model (GMM), and the scale-invariant features transformation (SIFT) of the foreground targets is extracted. Finally, global features and target features fusion through weights, calculating the difference between adjacent frames across multiple steps, generate a pattern distance map. The pattern distance map of the gradual transition and the cut detection is different; we can judge the gradual transition and the cut detection according to the pattern distance map. Experiments show that the proposed symmetry method improves by about 2% in recall and accuracy compared to other algorithms.

Composed image retrieval is a challenging task in the field of multi-modal learning, aiming at measuring the similarities between target images and query images with modification sentences. Most previous methods either construct feature composition for the query image and modification text or concentrate on extracting cross-modal alignments. However, these methods are prone to neglect the negative impacts of the mismatched correspondences between the hybrid-modal query and target, which could be discriminative when comparing similar instances. Besides, localized textual representations are not fully explored when learning similarities between the query and the target. To overcome the above issues, we propose a Negative-Sensitive Framework with Semantic Enhancement (NSFSE) for mining the adaptive boundaries between matched and mismatched samples with comprehensive consideration of positive and negative correspondences. It can optimize the threshold dynamically based on distributions to explore the intrinsic characteristics of positive and negative correlations, which could further facilitate accurate similarity learning. A text-guided attention mechanism after infusing cross-modal affinities on localized word features is exploited in NSFSE to explore latent semantic-related visual similarity and cross-modal similarity simultaneously. The performance of extensive experiments and comprehensive analysis on three representative datasets CIRR, FashionIQ, and Fashion200 K demonstrate the effectiveness of negative mining of similarity with semantic enhancement in the proposed NSFSE.

Because of the random distribution of microblog public opinion data, it is difficult to mine, this paper proposes a microblog public opinion data mining algorithm based on multi vision clustering model. It constructs the phase space distribution structure model of microblog public opinion data, the fuzzy association rule distribution set of microblog public opinion data, analyses the high-order statistical characteristics of microblog public opinion data, and advances the data of fuzzy clustering center according to the different statistical characteristics Row partition block scheduling. The binary structure of microblog public opinion data is reconstructed in the virtual database, and multi angle fuzzy clustering is carried out according to the reconstruction results to realise the optimised mining of microblog public opinion data. The simulation results show that the mining time of this method is up to 4.13 MS and the mining accuracy is up to 100%.

In this paper, the research focused on wild and introduced cultivated flowers with multiple diseases such as Stephanitis, Sooty Mould, Xanthosis, and Leaf Blight, utilizing transfer learning and and data fusion technology to construct a plant disease detection model employing Faster R-CNN.The self-built data set collected during the flower growth cycle was trained and identified.To solve the problem of disease category imbalance in the actual collected data samples, the data of small category samples is enhanced from the perspective of category balance and label balance, and FocalLoss is used to improve the original classification loss function. Based on this self-built data set, the constructed IFRCNN disease detection model was compared with the SSD (Single Shot multibox Detector), ResNet18 and Yolov3 models. The results showed that for several common plant diseases in the dataset, the mAP of IFRCNN disease detection model was significantly higher than that of the other three models. It can effectively locate plant leaf disease areas, realize the detection of multiple diseases, and provide reference for accurate disease prevention and control.

Protein classification is a crucial field in bioinformatics. The development of a comprehensive tool that can perform feature evaluation, visualization, automated machine learning, and model interpretation would significantly advance research in protein classification. However, there is a significant gap in the literature regarding tools that integrate all these essential functionalities. This paper presents iProps, a novel Python-based software package, meticulously crafted to fulfill these multifaceted requirements. iProps is distinguished by its proficiency in feature extraction, evaluation, automated machine learning, and interpretation of classification models. Firstly, iProps fully leverages evolutionary information and amino acid reduction information to propose or extend several numerical protein features that are independent of sequence length, including SC-PSSM, ORDip, TRC, CTDC-E, CKSAAGP-E, and so forth; at the same time, it also implements the calculation of 17 other numerical features within the software. iProps also provides feature combination operations for the aforementioned features to generate more hybrid features, and has added data balancing sampling processing as well as built-in classifier settings, among other functionalities. Thus, It can discern the most effective protein class recognition feature from a multitude of candidates, utilizing three automated machine learning algorithms to identify the most optimal classifiers and parameter settings. Furthermore, iProps generates a detailed explanatory report that includes 23 informative graphs derived from three interpretable models. To assess the performance of iProps, a series of numerical experiments were conducted using two well-established datasets. The results demonstrated that our software achieved superior recognition performance in every case. Beyond its contributions to bioinformatics, iProps broadens its applicability by offering robust data analysis tools that are beneficial across various disciplines, capitalizing on its automated machine learning and model interpretation capabilities. As an open-source platform, iProps is readily accessible and features an intuitive user interface, ensuring ease of use for individuals, even those without a background in programming. The source code of the software is available for download at the following website: https://github.com/LigosQ/iProps and https://gitee.com/LigosQ/iProps .

With the increasing application scenarios of UAVs in industry, agriculture, military and other fields, the potential threats to national security and public security cannot be ignored. In addition, effective UAV detection and/or tracking is becoming an increasingly important security service. This paper integrates deep learning and image processing technology to conduct research in this context. In this paper, a transfer learning based UAV detection model (YOLOV5-UAV) is proposed. In order to reduce the influence of the amount of supervised data and the imbalance of target distribution on the performance of the model, the dataset is constructed based on self-shot videos and Internet downloaded videos in different natural scenes, combined with Mosaic data enhancement and adaptive scaling techniques. Therefore, the problem of data security is also effectively solved. Furthermore, real-time tests were carried out in two different time periods, namely day and night, from multiple scales, multiple perspectives and multiple natural scenes, for purpose of verifying the validity of the model. The applicability of different detection models is compared and analyzed for small target, moving background and weak contrast between UAV and background. The results show that YOLOV5-UAV model has a good performance in both detection accuracy and detection speed.