Workshop on Computer Vision for Bioanalytical Chemists: Classification and Detection of Amoebae Using Optical Microscopy Image Analysis with Machine Learning

The most extensively utilized and comprehensive common-sense cognitive engine in the ecosphere is artificial intelligence (AI). The cloud SaaS model and the concept of AI business platforms are practically affluent. It is about AI technologies that can work with other digital systems, so one of the most efficient strategies to protect plants from illnesses in a complicated environment is to detect infections early on. Plant disease detection has become digitized and data-driven as smart farming has expanded, which has the potential to make decisions automatically and intelligently, as well as smart analytics and management, thanks to continual improvements in the field of computer vision. AI-based machine learning (ML) and deep learning (DL) models have recently made substantial progress in the field of digital image processing. Researchers are eager to learn more about how to use a good ML or DL model to identify plant diseases, when pests attack plants and crops, it has an impact on the country’s agricultural output. Usually, farmers or professionals use their naked eyes to detect and identify diseases in plants. However, this procedure can be time-consuming, costly, and imprecise. Automatic detection using image processing techniques yields rapid and reliable results. Based on leaf image classification and deep convolutional networks, this research investigates a unique strategy for constructing a plant disease identification model. Computer vision advances have the potential to widen and improve precision plant protection while also growing the market for computer vision applications in precision agriculture. The novel training method and the technique employed allow for a rapid and painless installation of the system in practice. In this chapter, furthermore, the life cycle of the automatic plant disease detection approach which includes data collection, segmentation, feature extraction, and classification is also mentioned.

In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.

Object Detection Technology has been a subject to much research and development due to increasing use of images and videos as data sources and their huge number of applications. Traditional models for object detection had limitations in training and did not use transfer learning for their benefit. With the evolution of deep learning and Neural networks, newer and powerful tools have made way to achieve Object Detection in real-time, with the added advantage of transfer learning and detection of multiple instances of different classes of interest in the given image context. The proposed system is an Object Detection model based on the Single Shot Detector (SSD) algorithm trained with MobileNetV2 feature extraction that can be utilized and integrated in e-commerce, hospitality industry, security and surveillance, real estate, self-driving cars and floor inventory management.

Purpose: This study aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in modern agriculture, emphasising their potential to revolutionise traditional farming practices, enhance resource efficiency, and address global food security challenges. Design/methodology/approach: Through an extensive review of recent technological advancements and practical applications, this chapter examines the integration of AI and ML with human-centric agricultural practices. The review focuses on various domains such as yield prediction, pest and disease detection, crop monitoring, precision farming, and livestock management. Findings: The study identifies AI and ML as critical enablers of smart agriculture, facilitating real-time data analysis through advanced ML algorithms, computer vision, and sensor-based systems. These technologies provide actionable insights for farmers, improving decision-making processes and overall farm productivity. Furthermore, the paper underscores the importance of these tools in promoting sustainable and resilient farming systems. Practical implications: By showcasing how AI and ML can enhance agricultural efficiency and sustainability, this study offers valuable guidance for policymakers, agritech developers, and farming communities. It highlights the need for strategic implementation to maximise benefits while ensuring ethical and equitable technology access. Originality/value: This chapter contributes a timely and comprehensive perspective on AI-driven innovation in agriculture. It positions AI and ML not just as technological tools but as transformative forces with the potential to reshape global agricultural practices for a more food-secure and environmentally responsible future.Keywords:

Detecting objects from images is a challenging problem in the domain of computer vision and plays a very crucial role for wide range of real-time applications. The ever-increasing growth of deep learning due to availability of large training data and powerful GPUs helped computer vision community to build commercial products and services which were not possible a decade ago. Deep learning architectures especially convolutional neural networks have achieved state-of-the-art performance on worldwide competitions for visual recognition like ILSVRC, PASCAL VOC. Deep learning techniques alleviate the need of human expertise from designing the handcrafted features and automatically learn the features. This resulted into use of deep architectures in many domains like computer vision (image classification, visual recognition) and natural language processing (language modeling, speech recognition). Object detection is one such promising area immensely needed to be used in automated applications like self-driving cars, robotics, drone image analysis. This paper analytically reviews state-of-the-art deep learning techniques based on convolutional neural networks for object detection.

Weed diversity plays an important role in the functioning of agroecosystems. Moreover, a number of endangered/threatened plant species occur as weeds in arable fields and/or field boundaries. Agricultural intensification has imposed negative consequences on weed diversity in general, and the survival of the endangered/threatened plant species in particular. The objective of this review is to provide a theoretical framework for promoting cropland weed diversity through precision agriculture. A systematic review was conducted based on literature analysis, existing knowledge gaps, and current needs to identify a suitable approach for promoting cropland biodiversity while protecting crop yields. While nonchemical weed management methods and economic threshold–based approaches are touted to improve weed diversity, they are either ineffective or insufficient for this purpose; long-term economic consequences and the risk of weed adaptation are major concerns. A plant functional trait-based approach to promoting weed diversity, one that considers a plant’s ecosystem service potential and competitiveness with the crop, among other factors, has been proposed by researchers. This approach has tremendous potential for weed diversity conservation in commercial production systems, but field implementation has been limited thus far due to our inability to selectively control weeds at the individual-plant level. However, recent advancements in computer vision, machine learning, and site-specific weed management technologies may allow for the accurate elimination of unwanted plants while retaining the important ones. Here, we present a novel framework for the utilization of precision agriculture for the conservation of cropland weed diversity, including the protection of endangered/threatened plant species, while protecting crop yields. This approach is the first of its kind in which the control priority is ranked on an individual-plant basis, by integrating intrinsic weed trait values with field infestation characteristics, while management thresholds are tailored to specific goals and priorities.

Transformer has achieved great success in computer vision, while how to split patches in an image remains a problem. Existing methods usually use a fixed-size patch embedding which might destroy the semantics of objects. To address this problem, we propose a new Deformable Patch (DePatch) module which learns to adaptively split the images into patches with different positions and scales in a data-driven way rather than using predefined fixed patches. In this way, our method can well preserve the semantics in patches. The DePatch module can work as a plug-and-play module, which can easily be incorporated into different transformers to achieve an end-to-end training. We term this DePatch-embedded transformer as Deformable Patch-based Transformer (DPT) and conduct extensive evaluations of DPT on image classification and object detection. Results show DPT can achieve 81.9% top-1 accuracy on ImageNet classification, and 43.7% box mAP with RetinaNet, 44.3% with Mask R-CNN on MSCOCO object detection. Code has been made available at: https://github.com/CASIA-IVA-Lab/DPT .

Deep (machine) learning in recent years has significantly increased the predictive modeling strengths for applications in the areas of computer vision, speech recognition, and natural language processing. Being heavily data-driven, conventional general-purpose deep learning methods mainly rely on training data to learn task-specific data representations. In this thesis, we focus on leveraging novel learning algorithms in deep learning to tackle visual recognition challenges at the three key levels -- namely pixel, object, and image levels. Throughout the thesis, we demonstrate how each and every of our proposed methods can address a specific visual recognition problem. Firstly, we apply deep learning to improve the saliency detection performance of deep networks. A coarse saliency map of the input image's salient object is initially predicted by a convolutional-deconvolutional network (or equivalently a fully convolutional network). Following that, the coarse saliency map is sequentially refined by a separate network called Recurrent Attentional Convolutional-Deconvolutional Network (RACDNN) that shares its network weights across its sequential steps. At every sequential (temporal) step, the same RACDNN is applied to a newly selected/attended image sub-region and it performs saliency map refinement for that particular sub-region. Despite having many learnable network parameters and no access to additional training data, RADCNN can be trained well and it outperforms the "single-step" (coarse saliency detection) baseline and state-of-the-art saliency detection methods by large margins, on several saliency detection benchmark datasets. Secondly, we introduce auto-encoder weight transfer networks (AE-WTN+), a deep model for scaling up object detection. Conventional WTN transfers object class knowledge from the classification weights of a pretrained large-scale image classification network, to the classification weights of an object detection network. However, they suffer from under-fitting and are not able to preserve the rich object class information of novel classes. In this work, we propose a new WTN method -- AE-WTN+ which makes use of normalization techniques to ease network optimization. Inputs and intermediate features are normalized to improve trainability for reduced under-fitting. More importantly, a novel auto-encoding (reconstruction) loss is proposed to encourage information preservation of all classes in the weight predictions of AE-WTN+. This enables the predicted weights of AE-WTN+ to maintain the class discriminability and neighbourhood relationships contained in the pre-trained large-scale classification weights. Experiments on large-scale detection datasets validate the effectiveness of AE-WTN+ on both seen and novel classes. Thirdly, we improve the efficiency of cross-layer network connections for very deep convolutional network in the context of image classification. This gives rise to a new convolutional network architecture called Deluge Networks (DelugeNets) which pass the output (feature activations) of any preceding composite network layer as direct inputs to all succeeding composite layers. For the incoming feature activations of preceding composite layers, the succeeding composite layers first apply rather lightweight cross-layer depthwise convolutional weights on them for feature aggregation. The heavy lifting is carried out by the last convolutional weight layer in each preceding composite layer that performs cross-channel convolutions. The proposed technique enables unimpeded information flow among many layers in very deep DelugeNets with great efficiency. Experiments on small-scale and large-scale datasets show that DelugeNets perform competitively to state-of-the-art deep convolutional networks at lower computational and parameter costs. Fourthly, we propose Stochastic Downsampling (SDPoint), a method to overcome a major limitation suffered by conventional deep convolutional networks -- their computational costs during inference are fixed and cannot be changed. SDPoint downsamples the feature maps at a randomly selected point (layer index) in the network hierarchy during training. The many downsampling configurations known as SDPoint instances each entail a unique downsampling point and downsampling ratio. The same convolutional network weights are shared across the different SDPoint instances that downsample feature maps differently. During inference, the computational cost of a network trained with SDPoint can be instantaneously adjusted to fit a given computational budget, by manually selecting an appropriate SDPoint instance. Additionally, sharing weights between different feature map scales provides significant regularization benefits, making the networks trained with SDPoint less sensitive to scales and more competent. In image classification experiments, SDPoint demonstrates significant cost-accuracy and computational advantages, over independently-trained (non-weight-shared) baselines with various inference costs.

In a near of wide spread technological change that has givena positive impact to the society andhelpedinbuildingauser-friendly environment, object detection framework, an importantpart of Computer Vision (CV) plays a vital role. Starting fromasimpleautomaticattendancesystemforstudentsusingfacedetection, recognizing the presence of tumors in medical images,helping with automatic surveillance of cctv cameras to identifypeoplewhobreakstrafficrulescausingroadaccidentstobeingthecentral mechanism behind self-driving cars, object detection haswide range of applications and assist building an easy to cope withsmart environments. This in turn urges the need to evaluate theperformanceofthetechniquesbehindtheseframeworks.Thecentralideabehindthemodern-dayobjectdetectionandclassification is Convolutional Neural Network (CNN) which triesto mimic the occipital lobe, the visual cortex of the human brain.CNNhaswiderangeofvariationsandhascomethroughalongwaystarting from basic CV techniques like Scale Invariant FeatureTransform(SIFT),HistogramsofOrientedGradientsn(HOG)tillRegionbasedCNN’s(R-CNN).Theperformanceofeachandeverymethodthathas led throughthe evolutionofobjectdetectionmethods, its advantages and the disadvantages which has pavedway for the innovation of next technique has been discussed andrepresentedindetail.

Abstract: While drone delivery seemed ludicrous a few years ago, it is now a reality in many parts of the United States and throughout the world. Favourable development conditions, because of the FAA's more relaxed and transparent standards, as well as a rise in demand for autonomous last-mile delivery, are propelling drone delivery technology ahead. Consumers who have had the opportunity to experience drone delivery say they prefer the comfort, speed, and positive environmental effect of drone deliveries. Thus, it is very crucial to implement computer vision to increase the efficiency and safety of these delivery drones – and we believe that Jetson toolkit is a fantastic employable solution. Drones now have the processing power for improved object detection and tracking thanks to advances in computer vision and remote sensing. As a result, structured datasets can be used to teach deep learning-based unmanned aerial vehicles to detect objects while in surveillance mode. DNN-based drones have an important function: 3D object detection. After acquiring relevant data into the app, the camera with a trained computer vision model may scan an object utilising 3D object detection. This enables businesses to detect possible risks or things of concern in real time

Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was used for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic curve and the associated area under the curve were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the subgroup of normal wrist radiographs and 91.3% among the subgroup of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, a specificity of 93.3%, and an accuracy of 93.4%. The area under the curve was 0.986. The authors have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.

Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accuracy, false positive rate, number of detected objects, and mean intersection over union (mIoU). Our results show that our approach achieves a mean average precision of 0.76 on the COCO dataset and a real-time performance of 40 frames per second on a single GPU. Additionally, our approach achieves an object detection accuracy of 93.5%, a false positive rate of 6.5%, and a mean intersection over union of 0.65. Our proposed approach shows promising results for real-time object detection and recognition and can be applied to various real-world applications.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term ‘deep learning’, and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.

Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring. Written by a team of international experts, this edited book covers the state-of-the-art of advanced research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real world applications including object detection, biometrics, Deepfake detection, sentiment and emotion analysis, traffic enforcement camera monitoring, vehicle control and aerial remote sensing imagery. The book will be useful for industry and academic researchers, scientists and engineers in the fields of computer vision, machine vision, image processing and recognition, multimedia, AI, machine and deep learning, data science, biometrics, security, and signal processing. It will also make a great course reference for advanced students and lecturers in these fields of research.