Automatic Counting Research Articles

Fluorescent identification of axons, dendrites and soma of neuronal retinal ganglion cells with a genetic marker as a tool for facilitating the study of neurodegeneration

AbstractThis study characterizes a fluorescent Slc17a6‐tdTomato neuronal reporter mouse line with strong labeling of axons throughout the optic nerve, of retinal ganglion cell (RGC) soma in the ganglion cell layer (GCL), and of RGC dendrites in the inner plexiform layer (IPL). The model facilitated assessment of RGC loss in models of degeneration and of RGC detection in mixed neural/glial cultures. The tdTomato signal showed strong overlap with >98% cells immunolabeled with RGC markers RBPMS or BRN3A, consistent with the ubiquitous presence of the vesicular glutamate transporter 2 (VGUT2, SLC17A6) in all RGC subtypes. There was no cross‐labeling of ChAT‐positive displaced amacrine cells in the GCL, although some signal emanated from the outer plexiform layer, consistent with horizontal cells. The fluorescence allowed rapid screening of RGC loss following optic nerve crush and intraocular pressure (IOP) elevation. The bright fluorescence also enabled non‐invasive monitoring of extensive neurite networks and neuron/astrocyte interactions in culture. Robust Ca2+ responses to P2X7R agonist BzATP were detected from fluorescent RGCs using Ca2+‐indicator Fura‐2. Fluorescence from axons and soma was detected in vivo with a confocal scanning laser ophthalmoscope (cSLO); automatic RGC soma counts enhanced through machine learning approached the numbers found in retinal wholemounts. Controls indicated no impact of Slc17a6‐tdTomato expression on light‐dependent neuronal function as measured with a microelectrode array (MEA), or on retinal structure as measured with optical coherence tomography (OCT). In summary, the bright fluorescence in axons, dendrites and soma of ~all RGCs in the Slc17a6‐tdTomato reporter mouse may facilitate the study of RGCs.

Artesunate protects against a mouse model of cerulein and lipopolysaccharide‑induced acute pancreatitis by inhibiting TLR4‑dependent autophagy.

Severe acute pancreatitis (SAP) is a severe clinical condition associated with high rates of morbidity and mortality. Multiple organ dysfunction syndrome that follows systemic inflammatory response syndrome is the leading cause of SAP‑related death. Since the inflammatory mechanism of SAP remains unclear, there is currently a lack of effective drugs available for its treatment. Therefore, it is important to study effective therapeutic drugs and their molecular mechanisms based on studying the inflammatory mechanism of SAP. In the present study, in vivo, a mouse model of AP induced by cerulein (CR) combined with lipopolysaccharide (LPS) was established to clarify the therapeutic effect of artesunate (AS) in AP mice by observing the gross morphological changes of the pancreas and surrounding tissues, calculating the pancreatic coefficient, and observing the histopathology of the pancreas. The serum amylase activity in AP mice was detected by iodine colorimetry and the superoxide dismutase activity in the pancreas was detected by WST‑1 assay. The levels of proinflammatory cytokines in the serum, the supernatant of pancreatic tissue homogenates and the peritoneal lavage fluid were detected by ELISA assay. The total number of peritoneal macrophages was assessed using the cellular automatic counter, and the expression of proteins related to autophagy, and the TLR4 pathway was detected by immunohistochemistry and western blotting. In vitro, the effect of trypsin (TP) combined with LPS was observed by detecting the release of proinflammatory cytokine levels from macrophages by ELISA assay, and detecting the expression of proteins related to autophagy and the TLR4 pathway by immunofluorescence and western blotting. The present study revealed that AS reduced pancreatic histopathological damage, decreased pancreatic TP and serum amylase activities, increased superoxide dismutase activity, and inhibited pro‑inflammatory cytokine levels in a mouse model of AP induced by cerulein combined with lipopolysaccharide. In vitro, TP combined with LPS was found to synergistically induce pro‑inflammatory cytokine release from mouse macrophages and RAW264.7 cells, while AS could inhibit cytokine release. Furthermore, CR combined with LPS synergistically increased amylase activity in acinar cells, whereas AS decreased amylase activity. Autophagy serves an important role in the release of pro‑inflammatory cytokines. In the present study, it was revealed that the autophagy inhibitor LY294002 suppressed the release of pro‑inflammatory cytokines from macrophages treated with TP combined with LPS, and pro‑inflammatory cytokine release was not further reduced by AS combined with LY294002. Furthermore, AS not only inhibited the expression of important molecules in the Toll‑like receptor 4 (TLR4) signaling pathway, but also inhibited autophagy proteins and reduced the number of autolysosomes in mice with AP and in macrophages. In conclusion, these results suggested that AS may protect against AP in mice via inhibition of TLR4‑dependent autophagy; therefore, AS may be considered a potential therapeutic agent against SAP.

Automatic detection and counting of wheat spike based on DMseg-Count

The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments.

Automatic Passenger Counting System on Public Buses Using CNN YOLOv8 Model for Passenger Capacity Optimization

This study presents the development of an automatic passenger counting system for public buses using YOLOv8 based on Convolutional Neural Networks (CNN). The system detects and counts passengers in real-time to optimize bus capacity and enhance operational efficiency. Results indicate that the system achieves high accuracy in the front camera view (confidence score of 0.82). However, in the rear camera view the accuracy slightly decreases (confidence score of 0.76) due to object overlap, emphasizing the importance of proper camera placement. The system offers potential improvements in bus capacity management, reduced operational costs, and enhanced passenger comfort. These findings contribute to advancing smarter and more efficient public transportation systems.

The Relationship between Raised Mean Platelet Volume and Severity of Newly Diagnosed Acute Ischaemic Stroke

Introduction and Objective: Stroke is one of the leading causes of mortality and long-term disability throughout the world. Mean platelet volume (MPV), a marker of platelet activation, is calculated and provided by automatic blood cell count equipment during routine complete blood count analysis is a surrogate marker of platelet function and a potential mediator of the association between inflammation and thrombosis. So, to investigate MPV in ischaemic stroke and its role to discriminating more severe stroke from mild one the current study was done. Methods: This observational case control study was carried out in Department of Medicine, Dhaka Medical College Hospital (DMCH) from July 2015 to June 2016. 100 patients with attack of ischaemic stroke who presented within 24 hours of symptoms onset were divided into 2 groups (group-1: mRS score 0-2, and group-2: mRS score 3 or more) based on severity. Severity of ischaemic stroke was assessed by the modified Rankin Scale. At the same time 100 age and sex matched controls were taken. Blood samples were collected from both groups and MPV was measured. The results were statistically compared by SPSS 22.0. Statistical significance was accepted at <0.05. Results: The MPV values were more in case group in comparison to control group (10.88±1.23 vs 10.03±1.03; z score-5.31; p value <0.01). Comparison between the 2 groups revealed that MPV value was higher and more significant in group 2 of cases than group 1(10.15± 0.739 in group 1 versus 11.63± 1.04 in group having 2; z score-7.7, p value <0.001). 6 patients of group 2, who could not overcome the situation and died within 24 hours of admission had MPV value of 12.92±0.30. After controlling the risk profiles associated with ischaemic stroke by means of binary logistic regression model, the effect of MPV in ischaemic stroke remained statistically significant (OR: 4.418, 95% CI 2.468-7.908, p= 0.000). ROC curve was 0.85 and standard error 0.039 (95% CI: 0.779-0.932; p<0.01), ...

Automatic assessment of nailfold capillaroscopy software: a pilot study.

Capillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capillary counting and image classification as normal or pathological. The study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis or scleroderma spectrum diseases and healthy people. Dinolite MEDL4N Pro was used to perform capillaroscopy. Each image was analysed manually and described using working software. The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen; 10-fold cross-validation with the validation and test set was performed, using the Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For the calculation of 1 mm capillaries, an additional detection mechanism was designed. The results obtained under neural network training were compared to the results obtained in manual analysis. The sensitivity of the automatic tool relative to manual assessment in classification of correct vs. pathological images was 89.0%, specificity 89.4% for the training group, in validation 89.0% and 86.9% respectively. For the average number of capillaries in 1 mm the precision of real images detected within the region of interest was 96.48%. The pilot software for fully automatic capillaroscopic image assessment can be a useful tool for the rapid classification of a normal and altered capillaroscopy pattern. In addition, it allows one to quickly calculate the number of capillaries. In the future, the tool will be developed and will make it possible to obtain full imaging characteristics independent of the experience of the examiner.

Evaluating the accuracy of an automatic counting system to detect dispensing of hand hygiene product

BackgroundHand hygiene (HH) is an essential element of infection prevention and control programs. Direct observation of adherence to the 5 moments for HH is considered the gold standard in compliance monitoring. However, as direct observation introduces potential bias, other strategies have been proposed to supplement HH compliance data in healthcare facilities. This study evaluated the accuracy of an automatic counting system (MEZRIT™) to detect when a HH product (soap or alcohol-based hand rub) was dispensed, and thus measure product usage as opposed to compliance with the 5 moments for HH. MethodsA quasi-experimental study was conducted in a nursing simulation lab where seven participants undertook basic nursing tasks which included performing HH. Sensors were attached to soap and alcohol-based hand rub dispensers to record the time at which a product was dispensed. HH events were video recorded (time-stamped) and validated against timestamps from the automatic counting system. Results260 HH events were detected by the automatic counting system and confirmed by video recordings. 5182 non-HH events were calculated from analysis of the video recordings. The automatic counting system had 90 % sensitivity (95%CI 85.8–93.1 %), and 100 % specificity (95%CI 99.9–100 %). This model generated a positive predictive value of 100 % (95%Cl 98.4–100 %), and a negative predictive value of 99.5 % (95%CI 99.3–99.7 %). ConclusionThe MEZRIT™ system accurately identified 90 % of HH events and excluded 100 % of non-HH events. The real-time monitoring of HH product usage may be beneficial in responding quickly to changes in product usage.

Automatic Apple Detection and Counting with AD-YOLO and MR-SORT.

In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model's ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.

Automated counting and classifying Daphnia magna using machine vision

Daphnia magna (D. magna) is a model organism widely used in aquatic ecotoxicology research due to its sensitivity to environmental changes. The survival and reproduction rates of D. magna are easily affected by toxic environments. However, their small size, fragility, and transparency, especially in neonate stages, make them challenging to count accurately. Traditionally, counting adult and neonate D. magna relies on manual separation and visual observation, which is not only tedious but also prone to inaccuracies. Previous attempts to aid counting with optical sensors have faced issues such as inducing stress damage due to vertical movement and an inability to distinguish between adults and neonates. With the advancement of deep learning technologies, our study employs a simple light source culture device and utilizes the Mask2Former model to analyze D. magna against the background. Additionally, the U-Net model is used for comparative analysis. We also applied OpenCV technology for automatic counting of adult and neonate D. magna. The model's results were compared against manual counting performed by experienced technicians. Our approach achieves an average relative accuracy of 99.72 % for adult D. magna and 98.30 % for neonate. This method not only enhances counting accuracy but also provides a fast and reliable technique for studying the survival and reproduction rates of D. magna as a model organism.

A comparison of actual and perceived risk of apprehension for speeding in Norway

This paper compares actual and perceived risk of apprehension for speeding in Norway. Actual risk of apprehension was estimated by relying on data on the number of citations for speeding and the percentage of vehicles speeding when passing automatic traffic counting stations. It was defined as the number of detected violations per million kilometres driven while committing a violation. Perceived risk of apprehension was estimated as a mean annual frequency of getting detected by the police, based on survey answers given by samples of drivers surveyed in 2010, 2014 and 2024. Actual risk of apprehension was converted into a mean annual frequency of detection by relying on estimates of the mean annual driving distance. Thus, perceived mean annual frequency of detection could be compared to actual mean annual frequency of detection. Drivers were found to overestimate the risk of apprehension considerably, but the size of the overestimation declined from 2010 to 2014 and further again to 2024. In 2024, mean perceived risk of apprehension was about 2.4 times higher than actual risk of apprehension. Drivers were also found to overestimate the number of speed cameras deployed in Norway. Only a small minority of drivers had a correct perception of how the risk of apprehension for speeding varied according to the level of speeding. The decisions drivers make about speeding are based on their perceived risk of apprehension; hence it is advantageous to compliance that drivers overestimate the risk of apprehension.

Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.

The counting and characterization of neurons in primary cultures have long been areas of significant scientific interest due to their multifaceted applications, ranging from neuronal viability assessment to the study of neuronal development. Traditional methods, often relying on fluorescence or colorimetric staining and manual segmentation, are time consuming, labor intensive, and prone to error, raising the need for the development of automated and reliable methods. This paper delves into the evaluation of three pivotal deep learning techniques: semantic segmentation, which allows for pixel-level classification and is solely suited for characterization; object detection, which focuses on counting and locating neurons; and instance segmentation, which amalgamates the features of the other two but employing more intricate structures. The goal of this research is to discern what technique or combination of those techniques yields the optimal results for automatic counting and characterization of neurons in images of neuronal cultures. Following rigorous experimentation, we conclude that instance segmentation stands out, providing superior outcomes for both challenges.

A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.

Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution.

Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure-property relationships.

YOLOv7-KDT: An ensemble model for pomelo counting in complex environment

Pomelo counting recognition in complex environments faces many difficulties, mainly including changes in illumination, occlusion, and complex backgrounds. These factors contribute to the poor recognition performance of early models such as Faster R-CNN, SSD, and Efficientdet. This study optimized the YOLOv7 object detection network through several strategies for this issue. The main optimization strategies are: (1) Introducing the SEAM module to improve the detection ability for occluded objects; (2) Using the AIFI attention mechanism to enhance the network’s recognition of small objects; (3) Adopting the LAMP pruning technique to reduce model complexity; (4) Utilizing multi-teacher knowledge distillation for model compression and performance enhancement. The results show that under the assisted learning of the student module by the multi-teacher module distillation, the proposed method improves mAP by more than 1.9% compared to the aforementioned early models and other networks. After LAMP pruning, the model size is reduced by at least 93.6%. After knowledge distillation processing, its mAP is increased from 84.2% to 91.1%, achieving high-precision pomelo counting recognition. This study effectively solves automatic pomelo recognition and counting in complex environments. The method can be generalized to other agricultural product detection and statistics scenarios, having broad application prospects.

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults

BackgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.MethodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps—one BGM, one BGM + VECT and the combination of four sticky traps (STs)—were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.ResultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.ConclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.Graphical

Video-based automatic people counting for public transport: On-bus versus off-bus deployment

Interest in Automatic People Counting (APC) for crowd detection and management is rapidly growing. While a range of Internet of Things (IoT) sensors and systems exist, video analytics is emerging as a particularly attractive option — especially for applications where more traditional methods of people counting are not available, unreliable or expensive. In this paper we focus on automatic people counting in the public transport context – specifically, rail replacement bus services – in which bus companies are typically contracted to provide bus services to replace train services during periods of planned and unplanned line disruption. This presents a particularly compelling use-case for video-based people counting, while also presenting unique challenges. Field trials are thus vital to the proper assessment of video-based APC solutions, however remain relatively scarce in the literature. While datasets to support research and benchmarking exist, these do not capture the intrinsic complexities of real-world deployment and the implications of selected configurations — in particular, on-vehicle versus off-vehicle use cases. In this paper, we evaluate our own video-based APC solution, representative of state-of-the-art approaches in the literature, in two separate extensive (i.e, multi-day) metropolitan field trials covering both on and off-bus use-cases. Through real-world deployment of the system in both settings, we highlight key differences with respect to APC accuracy, as well as other practical considerations, and the validity of underlying assumptions in both on and off-bus scenarios.

Image Recognition for Floating Waste Monitoring in a Traditional Surface Irrigation System

In the traditional surface irrigation system of Vega Baja del Segura (Spain), large amounts of floating waste accumulate at certain points of the river, irrigation channels and drainage ditches, causing malfunctioning of the irrigation network and rising social problems related to the origins of waste. This work proposes a standardized and quick methodology to characterize the floating waste to detect changes in its amount and components. A dataset was created with 477 images of floating plastic items in different environments and was used for training an algorithm based on YOLOv5s. The mean Average Precision of the trained algorithm was 96.9%, and the detection speed was 81.7 ms. Overhead photographs were taken with an unmanned aerial vehicle at strategic points of the river and channels, and its automatic count of floating objects was compared with their manual count. Both methods showed good agreement, confirming that water bottles were the most abundant (95%) type of floating waste. The automatic count reduced the required time and eliminated human bias in image analysis of the floating waste. This procedure can be used to test the reach of corrective measures implemented by local authorities to prevent floating waste in the river.

Simultaneous enumeration of yeast and bacterial cells in the context of industrial bioprocesses

Abstract In scenarios where yeast and bacterial cells coexist, it is of interest to simultaneously quantify the concentrations of both cell types, since traditional methods used to determine these concentrations individually take more time and resources. Here, we compared different methods for quantifying the fuel ethanol Saccharomyces cerevisiae PE-2 yeast strain and cells from the probiotic Lactiplantibacillus plantarum strain in microbial suspensions. Individual suspensions were prepared, mixed in 1:1 or 100:1 yeast-to-bacteria ratios, covering the range typically encountered in sugarcane biorefineries, and analyzed using bright field microscopy, manual and automatic Spread-plate and Drop-plate counting, flow cytometry (at 1:1 and 100:1 ratios), and a Coulter counter (at 1:1 and 100:1 ratios). We observed that for yeast cell counts in the mixture (1:1 and 100:1 ratios), flow cytometry, the Coulter counter, and both Spread-plate options (manual and automatic CFU counting) yielded statistically similar results, while the Drop-plate and microscopy-based methods gave statistically different results. For bacterial cell quantification, the microscopy-based method, Drop-plate, and both Spread-plate plating options and flow cytometry (1:1 ratio) produced no significantly different results (P &amp;gt; 0.05). In contrast, the Coulter counter (1:1 ratio) and flow cytometry (100:1 ratio) presented results statistically different (P &amp;lt; 0.05). Additionally, quantifying bacterial cells in a mixed suspension at a 100:1 ratio wasn't possible due to an overlap between yeast cell debris and bacterial cells. We conclude that each method has limitations, advantages, and disadvantages.

Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees

The identification of individual trees is an important research topic in forestry, remote sensing and computer vision. It represents a tool for effectively and efficiently managing and maintaining forests and orchards. However, this task is not as simple as it seems; tree detection and counting can be time consuming, cost-prohibitive and accuracy-limited, especially if performed manually on a large scale.The availability of very high-resolution UAV imagery with remote sensing can make the counting process easier, faster and more precise. With the development of technology, this process can be made more automated by using intelligent algorithms such as CNN.This work presents an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) approach that combines CNNs with OBIA to automatically detect and count olive trees from Phantom4 advanced drone imagery. Initially, The CNN-based classifier was created, trained, validated, and applied to generate the Olive trees probability maps on the ortho-photo. The post-classification refinement based on OBIA was then conducted. A super-pixel segmentation and the Excess Green index were performed and a detailed accuracy analysis has been carried out to establish the suitability of the proposed method.The application to a RGB ortho-mosaic of an olive grove, in the east region of Morocco was successful using a manually elaborated training dataset of 4500 images of 24×24 pixels. Finally, the CNN detected and counted 2934 olive trees on the ortho-photo, achieving an overall accuracy of 97 % and 99 % after the OBIA refinement. The results of the proposed OBIA-CNN method were also compared with the classification results of using the Template matching technique, CNN method alone, and OBIA analysis alone to evaluate the performance of the approach. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of olive trees to support the accurate and sustainable agricultural monitoring.

Advancing primate surveillance with image recognition techniques from unmanned aerial vehicles.

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub-nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV-based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research.