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Remote sensing technology is vital for precision agriculture, aiding in early issue detection, resource management, and environmentally friendly practices. Recent advances in remote sensing technology and data processing have propelled unmanned aerial vehicles (UAVs) into valuable tools for obtaining detailed data on plant diseases with high spatial, temporal, and spectral resolution. Given the growing body of scholarly research centered on UAV-based disease detection, a comprehensive review and analysis of current studies becomes imperative to provide a panoramic view of evolving methodologies in plant disease monitoring and to strategically evaluate the potential and limitations of such strategies. This study undertakes a systematic quantitative literature review to summarize existing literature and discern current research trends in UAV-based applications for plant disease detection and monitoring. Results reveal a global disparity in research on the topic, with Asian countries being the top contributing countries (43 out of 103 papers). World regions such as Oceania and Africa exhibit comparatively lesser representation. To date, research has largely focused on diseases affecting wheat, sugar beet, potato, maize, and grapevine. Multispectral, reg-green-blue, and hyperspectral sensors were most often used to detect and identify disease symptoms, with current trends pointing to approaches integrating multiple sensors and the use of machine learning and deep learning techniques. Future research should prioritize (i) development of cost-effective and user-friendly UAVs, (ii) integration with emerging agricultural technologies, (iii) improved data acquisition and processing efficiency (iv) diverse testing scenarios, and (v) ethical considerations through proper regulations. [ABSTRACT FROM AUTHOR]

Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa's northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models' effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models' outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew's correlation coefficient, and Cohen's Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics. [ABSTRACT FROM AUTHOR]

The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases. [ABSTRACT FROM AUTHOR]

Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread. [ABSTRACT FROM AUTHOR]

At the time the world is attempting to get over the damage caused by the COVID-19 spread, the monkeypox virus threatens to evolve into a global pandemic. Human monkeypox was first recognized in Africa and has recently emerged in 103 countries outside Africa. However, monkeypox diagnosis in an early stage is difficult because of the similarity between it, chickenpox, cowpox and measles. In some cases, computer-assisted detection of monkeypox lesions can be helpful for quick identification of suspected cases. Infected and uninfected cases have added to a growing dataset that is publicly accessible and may be utilized by machine and deep learning to predict the suspected cases at an early stage. Motivated by this, a diagnostic framework to categorize the cases of patients into four categories (i.e., normal, monkeypox, chicken pox and measles) is proposed. The diagnostic framework is a hybridization of pre-trained Convolution Neural Network (CNN) models, machine learning classifiers and a metaheuristic optimization algorithm. The hyperparameters of the five pre-trained models (i.e., VGG19, VGG16, Xception, MobileNet and MobileNetV2) are optimized using a Harris Hawks Optimizer (HHO) metaheuristic algorithm. After that, the features can be extracted from the feature extraction and reduction layers. These features are classified using seven machine learning models (i.e., Random Forest, AdaBoost, Histogram Gradient Boosting, Gradient Boosting, Support Vector Machine, Extra Trees and KNN). For each classifier, 10-fold cross-validation is used to train and test the classifiers on the features and the weighted average performance metrics are reported. The predictions from the pre-trained model and machine learning classifiers are then processed using majority voting. This study conducted the experiments on two datasets (i.e., Monkeypox Skin Images Dataset (MSID) and Monkeypox Images Dataset (MPID)). MSID dataset values 97.67%, 95.19%, 97.96%, 95.11%, 96.58%, 95.10%, 90.93% and 96.65% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. While for the MPID dataset, values of 97.51%, 94.84%, 94.48%, 94.96%, 96.66%, 94.88%, 90.45% and 96.69% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. [ABSTRACT FROM AUTHOR]

The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively. [ABSTRACT FROM AUTHOR]

Featured Application: Cut mark identification and analysis is a fundamental component for archaeological investigation. Cut mark analysis, however, has been the root of great debates, with some authors claiming to have the oldest cut marks in or outside of Africa. If these marks were to truly be anthropic in nature, then the repercussions of these findings would produce a paradigm shift for our understanding of human evolution. Unfortunately, the majority of methods available for cut mark classification are namely qualitative in nature. Here we provide a new, highly powerful artificially intelligent neural network classification model that can be used to quantitatively and more objectively overcome these issues, using 3D digital microscopy, Deep Learning and Geometric Morphometrics to obtain up to 100% accuracy in some cases. The concept of equifinality is currently one of the largest issues in taphonomy, frequently leading analysts to erroneously interpret the formation and functionality of archaeological and paleontological sites. An example of this equifinality can be found in the differentiation between anthropic cut marks and other traces on bone produced by natural agents, such as that of sedimentary abrasion and trampling. These issues are a key component in the understanding of early human evolution, yet frequently rely on qualitative features for their identification. Unfortunately, qualitative data is commonly susceptible to subjectivity, producing insecurity in research through analyst experience. The present study intends to confront these issues through a hybrid methodological approach. Here, we combine Geometric Morphometric data, 3D digital microscopy, and Deep Learning Neural Networks to provide a means of empirically classifying taphonomic traces on bone. Results obtained are able to reach over 95% classification, providing a possible means of overcoming taphonomic equifinality in the archaeological and paleontological register. [ABSTRACT FROM AUTHOR]

Featured Application: This work is applicable in computer science, software engineering and computational linguistic specifically in natural language processing. Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Africa. This study proposed novel word embeddings from syllable embeddings (WEFSE) for Swahili to address the concern of word representation for agglutinative and syllabic-based languages. Inspired by the learning methodology of Swahili in beginner classes, we encoded respective syllables instead of characters, character n-grams or morphemes of words and generated quality word embeddings using a convolutional neural network. The quality of WEFSE was demonstrated by the state-of-art results in the syllable-aware language model on both the small dataset (31.229 perplexity value) and the medium dataset (45.859 perplexity value), outperforming character-aware language models. We further evaluated the word embeddings using word analogy task. To the best of our knowledge, syllabic alphabets have not been used to compose the word representation vectors. Therefore, the main contributions of the study are a syllabic alphabet, WEFSE, a syllabic-aware language model and a word analogy dataset for Swahili. [ABSTRACT FROM AUTHOR]

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved. [ABSTRACT FROM AUTHOR]

The availability of free and open data from Earth observation programmes such as Copernicus, and from collaborative projects such as Open Street Map (OSM), enables low cost artificial intelligence (AI) based monitoring applications. This creates opportunities, particularly in developing countries with scarce economic resources, for large–scale monitoring in remote regions. A significant portion of Earth's surface comprises desert dune fields, where shifting sand affects infrastructure and hinders movement. A robust, cost–effective and scalable methodology is proposed for road detection and monitoring in regions covered by desert sand. The technique uses Copernicus Sentinel–1 synthetic aperture radar (SAR) satellite data as an input to a deep learning model based on the U–Net architecture for image segmentation. OSM data is used for model training. The method comprises two steps: The first involves processing time series of Sentinel–1 SAR interferometric wide swath (IW) acquisitions in the same geometry to produce multitemporal backscatter and coherence averages. These are divided into patches and matched with masks of OSM roads to form the training data, the quantity of which is increased through data augmentation. The second step includes the U–Net deep learning workflow. The methodology has been applied to three different dune fields in Africa and Asia. A performance evaluation through the calculation of the Jaccard similarity coefficient was carried out for each area, and ranges from 84% to 89% for the best available input. The rank distance, calculated from the completeness and correctness percentages, was also calculated and ranged from 75% to 80%. Over all areas there are more missed detections than false positives. In some cases, this was due to mixed infrastructure in the same resolution cell of the input SAR data. Drift sand and dune migration covering infrastructure is a concern in many desert regions, and broken segments in the resulting road detections are sometimes due to sand burial. The results also show that, in most cases, the Sentinel–1 vertical transmit–vertical receive (VV) backscatter averages alone constitute the best input to the U–Net model. The detection and monitoring of roads in desert areas are key concerns, particularly given a growing population increasingly on the move. [ABSTRACT FROM AUTHOR]