A Comparative Study of Physically Accurate Synthetic Shadow Datasets in Agricultural Settings with Human Activity.

Shadow, a natural phenomenon resulting from the absence of light, plays a pivotal role in agriculture, particularly in processes such as photosynthesis in plants. Despite the availability of generic shadow datasets, many suffer from annotation errors and lack detailed representations of agricultural shadows with possible human activity inside, excluding those derived from satellite or drone views. In this paper, we present an evaluation of a synthetically generated top-down shadow segmentation dataset characterized by photorealistic rendering and accurate shadow masks. We aim to determine its efficacy compared to real-world datasets and assess how factors such as annotation quality and image domain influence neural network model training. To establish a baseline, we trained numerous baseline architectures and subsequently explored transfer learning using various freely available shadow datasets. We further evaluated the out-of-domain performance compared to the training set of other shadow datasets. Our findings suggest that AgroSegNet demonstrates competitive performance and is effective for transfer learning, particularly in domains similar to agriculture.

A synthetic shadow dataset of agricultural settings.

Shadow, a natural phenomenon resulting from the absence of direct lighting, finds diverse real-world applications beyond computer vision, such as studying its effect on photosynthesis in plants and on the reduction of solar energy harvesting through photovoltaic panels. This article presents a dataset comprising 50,000 pairs of photorealistic computer-rendered images along with their corresponding physics-based shadow masks, primarily focused on agricultural settings with human activity in the field. The images are generated by simulating a scene in 3D modeling software to produce a pair of top-down images, consisting of a regular image and an overexposed image achieved by adjusting lighting parameters. Specifically, the strength of the light source representing the sun is increased, and all indirect lighting, including global illumination and light bouncing, is disabled. The resulting overexposed image is later converted into a physically accurate shadow mask with minimal annotation errors through post-processing techniques. This dataset holds promise for future research, serving as a basis for transfer learning or as a benchmark for model evaluation in the realm of shadow-related applications such as shadow detection and removal.

The Development of a Stereo Vision System to Study the Nutation Movement of Climbing Plants.

Climbing plants, such as common beans (Phaseolus vulgaris L.), exhibit complex motion patterns that have long captivated researchers. In this study, we introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision. Our approach involves two synchronized cameras, one lateral to the plant and the other overhead, enabling the simultaneous 2D position tracking of the plant tip. These data are then leveraged to reconstruct the 3D position of the tip. Furthermore, we investigate the impact of external factors, particularly the presence of support structures, on plant movement dynamics. The proposed method is able to extract the position of the tip in 86-98% of cases, achieving an average reprojection error below 4 px, which means an approximate error in the 3D localization of about 0.5 cm. Our method makes it possible to analyze how the plant nutation responds to its environment, offering insights into the interplay between climbing plants and their surroundings.

Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection.

Recent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550-900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7-layer network (CNN-7) and a deep 18-layer network (CNN-18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D-CNN-18, compared to 90% for 3D-CNN-7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting. PRACTICAL APPLICATION: Orange bruises can reduce the market value of food, which is why the food processing industry needs to carry out quality inspections. An effective way to perform this inspection is by using hyperspectral images that can be processed with 2D or 3D models, either with deep or shallow neural networks. The results of the comparison performed in this work can be useful for the development of more accurate and efficient bruise detection methods for fruit inspection.

Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models.

The present study aims to estimate nitrogen (N) content in tomato (Solanum lycopersicum L.) plant leaves using optimal hyperspectral imaging data by means of computational intelligence [artificial neural networks and the differential evolution algorithm (ANN-DE), partial least squares regression (PLSR), and convolutional neural network (CNN) regression] to detect potential plant stress to nutrients at early stages. First, pots containing control and treated tomato plants were prepared; three treatments (categories or classes) consisted in the application of an overdose of 30%, 60%, and 90% nitrogen fertilizer, called N-30%, N-60%, N-90%, respectively. Tomato plant leaves were then randomly picked up before and after the application of nitrogen excess and imaged. Leaf images were captured by a hyperspectral camera, and nitrogen content was measured by laboratory ordinary destructive methods. Two approaches were studied: either using all the spectral data in the visible (Vis) and near infrared (NIR) spectral bands, or selecting only the three most effective wavelengths by an optimization algorithm. Regression coefficients (R) were 0.864±0.027 for ANN-DE, 0.837±0.027 for PLSR, and 0.875±0.026 for CNN in the first approach, over the test set. The second approach used different models for each treatment, achieving R values for all the regression methods above 0.96; however, it needs a previous classification stage of the samples in one of the three nitrogen excess classes under consideration.

Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques.

Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.

A mapping study of ensemble classification methods in lung cancer decision support systems.

Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules. Graphical abstract Main features of the mapping study performed in ensemble classification methods applied on lung cancer decision support systems.

Design and Development of a Mobile App for Accessible Beach Tourism Information for People with Disabilities.

The global increase in the proportion of the population with disabilities has caused a greater awareness toward guaranteeing their use of public services. In particular, there is emphasis on the accessibility and inclusivity of tourism resources, to improve the enjoyment and well-being for people with motor disabilities. This paper presents a case study on accessibility to beaches in the Region of Murcia, Spain, which is one of the main tourist areas in the country. First, the most important elements that allow for the accessible use of beaches are analyzed and exposed in detail. Then, an extensive field-work in the area of interest has been carried out and its results are evaluated. Finally, the development of a new mobile app is described. The objective of this tool is to provide updated, accurate, and reliable accessibility information regarding the beaches. As a result, more than a third of the beaches analyzed had a high level of accessibility, while almost another third are totally inaccessible. The proposed application is a valuable tool, not only to help people with physical and motor disabilities, but also to raise awareness among local authorities to create and improve accessible services.

Sustainable Accessibility: A Mobile App for Helping People with Disabilities to Search Accessible Shops.

People with motor disabilities must face many barriers and obstacles in their daily lives, making it difficult to perform everyday tasks. The purpose of this work is to improve their living conditions by providing an app with accessibility information in an updated, reliable and friendly form. The development of the system integrates national and regional accessibility regulations, architectural aspects, with an extensive field work, and a sustainable software process. The levels of accessibility and the requirements of the application are defined in the first phases of the project. The field work included the evaluation of 357 commercial establishments in the city of Murcia, Spain, showing that only 25% have a good accessibility, 40% are practicable with help, and 35% are inaccessible shops. The proposed system achieves its objectives of being sustainable and helping in the accessibility. Besides, the system can be a great incentive for businesses to improve their accessibility conditions. In conclusion, new technologies must have a much more active role in the promotion of universal accessibility. These tools must also consider the necessary requirements of sustainable development.

Improving Accessibility for People with Disabilities: A Case Study on Inclusive Beach Tourism.

Disability is an important area in biomedical engineering. But research on disability should not only focus on the healthcare aspects, but also on the integration of people with disabilities in the cultural and social contexts, such as the existence of architectural elements that prevent the use of common public services. The present research aims to improve accessibility and enjoyment of people with physical and motor disabilities to the tourist resources of the area of interest. We present a case study focused on the beaches of the Region of Murcia, Spain. Both the architectural and technological aspects of accessibility are analyzed. From the architectural point of view, the method includes the definition of the parameters of accessibility, and the features required for the equipment according to the local and national regulations. From the technological point of view, the tools and requirements necessary to develop the system are presented.

Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review.

This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and answers seven research questions: publication trends, channels and venues; medical tasks undertaken; ensemble types proposed; single techniques used to construct the ensemble methods; rules used to draw the output of the ensemble; datasets used to build and evaluate the ensemble methods; and tools used. A total of 107 papers were chosen after a study selection process. Ensemble methods were applied to diabetes in 2003 for the first time. All medical tasks related to the diabetes disease were investigated, and the diagnosis task was the most frequently addressed activity by means of ensemble methods. The homogeneous ensembles were the most common in the literature. Moreover, decision trees and support vector machines were the most used techniques to build homogeneous and heterogeneous ensembles, respectively. The most frequently found combiner was the majority voting rule. Our findings suggest that ensemble classification methods yield better accuracy than single classifiers. This statement, however, requires an aggregation of the evidence reported in the literature by means of a systematic literature review.

A Computer-Aided Detection System for Digital Chest Radiographs.

Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.

Calcification detection of abdominal aorta in CT images and 3D visualization in VR devices.

Automatic calcification detection in abdominal aorta consists of a set of computer vision techniques to quantify the amount of calcium that is found around this artery. Knowing that information, it is possible to perform statistical studies that relate vascular diseases with the presence of calcium in these structures. To facilitate the detection in CT images, a contrast is usually injected into the circulatory system of the patients to distinguish the aorta from other body tissues and organs. This contrast increases the absorption of X-rays by human blood, making it easier the measurement of calcifications. Based on this idea, a new system capable of detecting and tracking the aorta artery has been developed with an estimation of the calcium found surrounding the aorta. Besides, the system is complemented with a 3D visualization mode of the image set which is designed for the new generation of immersive VR devices.

Follow-me: A new start-and-stop method for visual animal tracking in biology research.

Very frequently, research in biology and ethology requires visual tracking of live animals, such as insects, rodents and fish. The challenge is particularly difficult in the aquatic environment because of the light scattering effect of water: reflections and refractions, low contrast and resolution images, noise, and also due to the unpredictable animal behavior. This paper describes a new tracking method that is based in start and stop detection. When the animals stop or move very slowly, a precise tracker based on AdaBoost classifiers is applied. Otherwise, when they move faster, a more efficient process based on background detection is used. A control system is responsible for deciding which method should be used in each instant. The experimental results using sea cucumbers (Echinodermata, Holothuroidea), zebrafish and rodents, show the efficiency and accuracy of the proposed method, which is able to deal with complex situations.

Technical solutions for mitigating security threats caused by health professionals in clinical settings.

The objective of this paper is to present a brief description of technical solutions for health information system security threats caused by inadequate security and privacy practices in healthcare professionals. A literature search was carried out in ScienceDirect, ACM Digital Library and IEEE Digital Library to find papers reporting technical solutions for certain security problems in information systems used in clinical settings. A total of 17 technical solutions were identified: measures for password security, the secure use of e-mail, the Internet, portable storage devices, printers and screens. Although technical safeguards are essential to the security of healthcare organization's information systems, good training, awareness programs and adopting a proper information security policy are particularly important to prevent insiders from causing security incidents.