A Comprehensive Survey of Image Augmentation Techniques for Deep Learning

Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution. Then, the algorithms are split into three categories; model-free, model-based, and optimizing policy-based. The model-free category employs image processing methods while the model-based method leverages trainable image generation models. In contrast, the optimizing policy-based approach aims to find the optimal operations or their combinations. Furthermore, we discuss the current trend of common applications with two more active topics, leveraging different ways to understand image augmentation, such as group and kernel theory, and deploying image augmentation for unsupervised learning. Based on the analysis, we believe that our survey gives a better understanding helpful to choose suitable methods or design novel algorithms for practical applications.Comment: Revisio

Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges

The past decade has witnessed the rapid development of ML and DL methodologies in agricultural systems, showcased by great successes in variety of agricultural applications. However, these conventional ML/DL models have certain limitations: They heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, foundation models have demonstrated remarkable successes in language and vision tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture fields. Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, we present conceptual tools and technical background to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, we first review recent FMs in the general computer science domain and categorize them into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, we outline the process of developing agriculture FMs and discuss their potential applications in smart agriculture. We also discuss the unique challenges associated with developing AFMs, including model training, validation, and deployment. Through this study, we contribute to the advancement of AI in agriculture by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems.Comment: 16 pages, 2 figure

Embracing Limited and Imperfect Data: A Review on Plant Stress Recognition Using Deep Learning

Plant stress recognition has witnessed significant improvements in recent years with the advent of deep learning. A large-scale and annotated training dataset is required to achieve decent performance; however, collecting it is frequently difficult and expensive. Therefore, deploying current deep learning-based methods in real-world applications may suffer primarily from limited and imperfect data. Embracing them is a promising strategy that has not received sufficient attention. From this perspective, a systematic survey was conducted in this study, with the ultimate objective of monitoring plant growth by implementing deep learning, which frees humans and potentially reduces the resultant losses from plant stress. We believe that our paper has highlighted the importance of embracing this limited and imperfect data and enhanced its relevant understanding

Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning–based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets

Arp2/3 Complex Regulates Asymmetric Division and Cytokinesis in Mouse Oocytes

Mammalian oocyte meiotic maturation involves oocyte polarization and a unique asymmetric division, but until now, the underlying mechanisms have been poorly understood. Arp2/3 complex has been shown to regulate actin nucleation and is widely involved in a diverse range of processes such as cell locomotion, phagocytosis and the establishment of cell polarity. Whether Arp2/3 complex participates in oocyte polarization and asymmetric division is unknown. The present study investigated the expression and functions of Arp2/3 complex during mouse oocyte meiotic maturation. Immunofluorescent staining showed that the Arp2/3 complex was restricted to the cortex, with a thickened cap above the meiotic apparatus, and that this localization pattern was depended on actin. Disruption of Arp2/3 complex by a newly-found specific inhibitor CK666, as well as by Arpc2 and Arpc3 RNAi, resulted in a range of effects. These included the failure of asymmetric division, spindle migration, and the formation and completion of oocyte cytokinesis. The formation of the actin cap and cortical granule-free domain (CGFD) was also disrupted, which further confirmed the disruption of spindle migration. Our data suggest that the Arp2/3 complex probably regulates oocyte polarization through its effect on spindle migration, asymmetric division and cytokinesis during mouse oocyte meiotic maturation

Association of GDF-15 and Syntax Score in Patient with Acute Myocardial Infarction

Aims. GDF-15 is considered to be an important biomarker for cardiovascular events, but the differences in serum GDF-15 levels between acute myocardial infarction (AMI) patients and non-AMI patients warrant further investigation. Methods. A cohort of 409 subjects was enrolled in the current study. The Syntax score was calculated from the baseline coronary angiography results by using online methods. Blood samples were obtained at the start of the study for an assessment of GDF-15 by using ELISA methods. Results. Patients with AMI had significantly higher levels of serum GDF-15 (Wilcox test, P < 0.001), Syntax scores (Wilcox test, P = 0.006), and left ventricular ejection fractions (LEVF, Wilcox test, P< 0.001). However, no significant differences were present among the other clinical characteristics. The logistical regression analysis indicated that serum GDF-15 levels (P=0.01534) were independent predictors of non-AMI and AMI after adjusting for age, sex, smoking status, and LVEF. Conclusions. Elevated serum levels of GDF-15 are independently associated with the risk of MI, and GDF-15 may serve as a protective factor for MI in the cardiovascular system

Regulation of cell migration by dynamic microtubules

Microtubules define the architecture and internal organization of cells by positioning organelles and activities, as well as by supporting cell shape and mechanics. One of the major functions of microtubules is the control of polarized cell motility. In order to support the asymmetry of polarized cells, microtubules have to be organized asymmetrically themselves. Asymmetry in microtubule distribution and stability is regulated by multiple molecular factors, most of which are microtubule-associated proteins that locally control microtubule nucleation and dynamics. At the same time, the dynamic state of microtubules is key to the regulatory mechanisms by which microtubules regulate cell polarity, modulate cell adhesion and control force-production by the actin cytoskeleton. Here, we propose that even small alterations in microtubule dynamics can influence cell migration via several different microtubule-dependent pathways. We discuss regulatory factors, potential feedback mechanisms due to functional microtubule-actin crosstalk and implications for cancer cell motility

DataSheet_1_Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.zip

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning–based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.</p