A survey of the vision transformers and their CNN-transformer based variants

One of the most significant risks impacting crops is pests, which substantially decrease food production. Further, prompt and precise recognition of pests can help harvesters save damage and enhance the quality of crops by enabling them to take appropriate preventive action. The apparent resemblance between numerous kinds of pests makes examination laborious and takes time. The limitations of physical pest inspection are required to be addressed, and a novel deep-learning approach called the Faster-PestNet is proposed in this work. Descriptively, an improved Faster-RCNN approach is designed using the MobileNet as its base network and tuned on the pest samples to recognize the crop pests of various categories and given the name of Fatser-PestNet. Initially, the MobileNet is employed for extracting a distinctive set of sample attributes, later recognized by the 2-step locator of the improved Faster-RCNN model. We have accomplished a huge experimentation analysis over a complicated data sample named the IP102 and acquired an accuracy of 82.43%. Further, a local crops dataset is also collected and tested on the trained Faster-PestNet approach to show the generalization capacity of the proposed model. We have confirmed through analysis that the presented work can tackle numerous sample distortions like noise, blurring, light variations, and size alterations and can accurately locate the pest along with the associated class label on the leaf of numerous types and sizes. Both visual and stated performance values confirm the effectiveness of our model. 

Faster-PestNet: A Lightweight deep learning framework for crop pest detection and classification

Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

A two-stream network with complementary feature fusion for pest image classification

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management. 

Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety

Risk and reliability analysis for maritime autonomous surface ship: A bibliometric review of literature from 2015 to 2022.

Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large-scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a CNN and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DNVT combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modeling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89% and 74.20%, respectively, which is better than other state-of-the-art methods (shown in Table 5) and has a high practical application value. This article is protected by copyright. All rights reserved. 

An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification

Oil species identification based on fluorescence excitation-emission matrix and transformer-based deep learning.

In the maritime industry, unsafe behaviors exhibited by crew members are a significant factor contributing to shipping and occupational accidents. Among these behaviors, unsafe operation of mooring lines is particularly prone to causing severe accidents. Video-based monitoring has been demonstrated as an effective means of detecting these unsafe behaviors in real time and providing early warning to crew members. To this end, this paper presents a dataset comprising videos of unsafe mooring line operations by crew members on the M.V. YuKun. Additionally, we propose an unsafe behavior recognition model based on the improved You Only Look Once (YOLO)-v4 network. Experimental results indicate that the proposed model, when compared to other models such as the original YOLO-v4 and YOLO-v3, demonstrates a significant improvement in recognition speed by approximately 35% while maintaining accuracy. Additionally, it also results in a reduction in computation burden. Furthermore, the proposed model was successfully applied to an actual ship test, which further verifies its effectiveness in recognizing unsafe mooring operation behaviors. Results of the actual ship test highlight that the proposed model’s recognition accuracy is on par with that of the original YOLO-v4 network but shows an improvement in processing speed by 50% and a reduction in processing complexity by about 96%. Hence, this work demonstrates that the proposed dataset and improved YOLO-v4 network can effectively detect unsafe mooring operation behaviors and potentially enhance the safety of marine operations.

https://www.mdpi.com/2077-1312/11/2/291/pdf?version=1675085527

Recognition of Unsafe Onboard Mooring and Unmooring Operation Behavior Based on Improved YOLO-v4 Algorithm

Animal blood and semen analysis plays a significant role in national biological resource management, wildlife conservation, and customs security quarantine. Traditional blood analysis methods have disadvantages, such as complex sample preparation, time consumption, and false positives. Therefore, proposing a rapid and highly accurate analysis method is highly valuable. Raman spectroscopy has been widely used in blood analysis, and efficient and accurate analysis results can be obtained through the machine learning algorithm feature extraction. Recently, the transformer network structure was applied to Raman spectroscopy recognition. However, the multihead self-attention mechanism does not perform well in extracting local feature peaks, although it obtains global feature relations. This paper proposes a neural network based on the combination of one-dimensional convolution and multihead self-attention mechanism (Raman ConvMSANet) to identify 52 species of blood and semen Raman spectra. The network can achieve reliable identification effects in multiclassification and sample imbalance situations, and the average identification accuracy of blood and semen can reach more than 98.5%. The proposed network model can be applied not only to blood and semen identification but also to other biological fields. 

Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Bing Han

Author Tools

Chat about Author