Estimation Of Diameter Research Articles

Novel tools for neuronal activity, vessel diameter and red blood cell velocity imaging

Healthy brain function requires a delicate balance between neural activity and local vascular dynamics. Perturbations to this balance, such as vessel diameter changes or fluctuations in red blood cell velocity, are thought to underlie a number of neurodegenerative and neurovascular diseases, including migraine, stroke, and epilepsy. Here, for the first time, we demonstrate high fidelity simultaneous acquisition of vascular and neural circuit dynamics in freely-moving animals using our newly developed nVue dual color miniscope. Our system enables simultaneous imaging of blood flow and neural activity in both superficial and deep-brain structures in awake, behaving rodents. By multiplexing two LEDs at up to 100hz, the nVue enables direct, real-time visualization of both hemodynamics and calcium activity in two channels. Additionally, we have augmented the Inscopix Data Processing Software package to capture time-varying estimates of vessel diameter and red blood cell (RBC) velocity in specific vessels. Vessel diameter is estimated as the full-width half maximum of a Lorentzian function fit to vessel cross-section, while RBC velocity is calculated based on time-lagged correlations between seed-pixels within individual regions of interest and all immediately neighboring pixels. Distances between correlation peaks are then plotted against time. Manual annotation of blood flow data was performed to establish a ground truth against which to validate the algorithm. The nVue blood flow imaging application offers a powerful, turn-key, multisystem workflow for simultaneously interrogating hemodynamics and neural circuit function in freely behaving animals which will profoundly expand the neurovascular coupling toolkit not only for basic and translational research but also for the development of novel preclinical therapeutics. Commercial This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation

The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.

Estimation of the activity median aerodynamic diameter of plutonium particles using image analysis

ABSTRACT The aerodynamic radioactive median diameter (AMAD) and the absorption type are necessary information for internal exposure assessment. In 2017, at a plutonium-handling facility in the Oarai site of the Japan Atomic Energy Agency, an accidental contamination occurred, and five workers were contaminated with radioactive materials, including plutonium. In this study, some smear papers and a dust sampling filter were measured with the imaging plate, and some particles were observed with an electron microscope. The minimum AMADs for the two absorption types of plutonium were estimated for the conservative dose assessment. Two AMADs were calculated for Type M as plutonium nitrate and Type S as plutonium dioxide. Excluding the extremely large particle found in a smear sample, the AMAD estimation results showed that the minimum AMADs of plutonium nitrate from smear papers were 4.3–11.3 μm, while those of plutonium dioxide were 5.6–14.4 μm. Moreover, the minimum AMAD of plutonium nitrate from a dust sampling filter was 3.0 μm, while that of plutonium dioxide was 3.9 μm.

A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI).

In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.

Thermal Models of Asteroids with Two-band Combinations of Wide-field Infrared Survey Explorer Cryogenic Data

We used the reparameterized Near-Earth Asteroid Thermal Model to model observations of a curated set of over 4000 asteroids from the Wide-field Infrared Survey Explorer in two wavelength bands (W2–3 or W3–4) and compared the results to previous results from all four wavelength bands (W1–4). This comparison was done with the goal of elucidating unique aspects of modeling two-band observations so that any potential biases or shortcomings for planned two-band surveys (e.g., the NASA Near-Earth Object Surveyor Mission) can be anticipated and quantified. The W2–3 two-band fits usually yielded slightly smaller diameters than the four-band fits, with a median diameter difference of −10%, with the 5% and 95% quantiles of the distribution at −32% and −1.5%, respectively. We conducted similar comparisons for W3–4, in part because the longest wavelength bands are expected to provide the best two-band results. We found that the W3–4 two-band diameters are slightly larger than the four-band results, with a median diameter difference of 11% and the 5% and 95% quantiles of the distribution at −2.1% and 26%, respectively. The diameter uncertainty, obtained with bootstrap analysis, is larger by 30% and 35% (median values) for the W2–3 and W3–4 fits, respectively, than for the corresponding four-band fits. Using 23 high-quality stellar occultation diameters as a benchmark, we found that the median errors of W2–3 and W3–4 diameter estimates are −15% and +12%, respectively, whereas the median error of the four-band fits is 9.3%. Although the W2–3 and W3–4 diameters appear to have greater systematic errors and uncertainties than their four-band counterparts, two-band estimates remain useful because they improve upon diameter estimates obtained from visible photometry alone.

4D flow magnetic resonance imaging to assess right ventricular outflow tract in patients undergoing transcatheter pulmonary valve replacement

Introduction and objectivesMagnetic resonance imaging (MRI) including 4D flow is used before percutaneous pulmonary valve implantation (PPVI). As PPVI is limited by the size of the right ventricular outflow tract (RVOT), accurate sizing is needed to plan the intervention. The aim of this study was to compare different MRI modalities and invasive angiography to balloon sizing of RVOT. MethodsSingle-center prospective study of patients who underwent PPVI for isolated pulmonary regurgitation assessed by 4D flow MRI, 3D steady-state free precession/gradient echo (3D SSFP/GRE) and contrast magnetic resonance angiography. Balloon sizing was considered as the reference. ResultsA total of 23 adults were included (mean age, 38.4±12.5 years). Eighteen patients underwent successful primary PPVI. The average of the narrowest RVOT diameter was 25.4±4.3 mm by balloon sizing. Compared to balloon sizing, RVOT diameters were better correlated when estimated by systolic 4D flow MRI (r=0.89, P<.001) than by diastolic 4D flow MRI (r=0.71, P <.001), 3D contrast magnetic resonance angiography (r=0.73; P <.001) and 3D SSFP/GRE (r=0.50; P=.04) and was not significantly correlated when estimated by 2D in diastole and systole. The mean difference between systolic 4D flow MRI and balloon sizing was 0.2 mm (95%CI, –3.5 to 3.9 mm), whereas it was wider with other techniques. ConclusionsBeyond the quantification of pulmonary valve regurgitation, 4D flow allows accurate estimation of RVOT diameters, especially in systole, which is fundamental before planning PPVI.

Modeling compatible taper and stem volume of pure Scots pine stands in Northeastern Turkey

Compatible taper and volume equations for pure and natural Scots pine stands in the northeastern part of Turkey (Ardahan Province) were developed using a nonlinear mixed-effects modeling approach. Experimental data were obtained from 137 felled sample trees in different diameter and height classes. The most successful model (Jiang et al. 2005) explained 98.3% of the variance in stem diameter estimation and the RMSE, ME, MAE, AIC and BIC value obtained using this model were 1.955 cm, 0.043 cm, 1.300 cm, 9783.8 and 9812.6, respectively. Considering the criterion values of AIC, BIC and -2LnL, the model with random-effects in two parameters (b1 and b3) was the most successful for Scots pine. While the mixed model including random parameters did not completely solved the problem of the autocorrelation of errors in this study, the use of the autoregressive error structure AR(1) eliminated the autocorrelation in the residuals. In addition, the best estimation results among 20 different calibration options were obtained using the option of measuring two tree diameters at d1.30 and d5.30 with validation data. The most successful model explained 99.18% of the total variance in stem volume estimation in Scots pine.

A numerical and experimental study on positive diffusive ionization waves in different N2/O2 mixtures: the role of photoionization

A diffusive ionization wave can be generated by an ultrafast high voltage far exceeding the inception threshold, and is featured by its unique and repetitive conical morphology. A combinative experimental and numerical study of the diffusive ionization waves is conducted in this work to investigate the role of photoionization in different N2/O2 mixtures with oxygen concentrations of 20%, 2%, 0.2%, 1 ppm, and pure nitrogen. In all gas mixtures, the ionization wave first forms a spherical shape after its inception then a conical when it approaches the plane electrode. Compared with typical filamentary streamers and inception cloud generated by low overvoltage, photoionization in a diffusive ionization wave takes effects mainly before the formation of the spherical ionization wave, and affects slightly the propagation velocity, discharge morphology, and the width (diameter) of the ionization wave. When the pin-to-plane electrode gap distance is kept 16 mm, in the atmospheric pressure simulation with an 85 kV voltage pulse, the maximum ionization width decreases from 11.4 mm in the 20% mixture to 9.1 mm in pure nitrogen. In the 200 mbar pressure experiment with a 16 kV voltage pulse, the maximum ionization width decreases from 12.5 mm in the 20% mixture to 11.6 mm in pure nitrogen. E in the inception cloud diameter estimation function (D= 2 U E −1) is modified to estimate the width of the ionization wave during its spherical propagation stage. It is shown that the estimation results at 180–205 kV cm−1 are in good agreement with the simulation results at atmospheric pressure air.

Root Location and Root Diameter Estimation of Trees Based on Deep Learning and Ground-Penetrating Radar

A full understanding of the growth and distribution of tree roots is conducive to guiding precision irrigation, fertilization, and other agricultural work during agricultural production. Detecting tree roots with a ground-penetrating radar is a repeatable detection method that does no harm to the earth surface and tree roots. In this research, a rapid and accurate automatic detection was conducted on hyperbolic waveforms formed by root targets in B-scan images based on YOLOv5s. Following this, the regions of interest containing target hyperbolas were generated. Three or more coordinate points on the hyperbola were selected according to the three-point fixed circle (TPFC) method to locate the root system and estimate the root diameter. The results show that the accuracy of hyperbola detection using YOLOv5s was 96.7%, the recall rate was 86.6%, and the detection time of a single image was only 13 ms. In the simulation image, the TPFC method was used to locate the root system and estimate the root diameter through three different frequency antennas (500 MHz, 750 MHz, and 1000 MHz). A more accurate result was obtained when the antenna frequency was 1000 MHz, with the average distance error of root system positioning being 3.17 cm, and the slope and R2 of the linear fitting result between the estimated root diameter and the actual one being 1.029 and 0.987, respectively. Verified by the pre-buried root test and wilderness field test, both root localization and root diameter estimation in our research were proved to gain good results and conform to the rules found in simulation experiments. Therefore, we believe that this method can quickly and accurately detect the root system, locate and estimate the root diameter, and provide a new perspective for the non-destructive detection of the root system and the three-dimensional reconstruction of the root system.

Estimating the amount of British Columbia’s “big-treed” old growth: Navigating messy indicators

British Columbia’s (BC) diverse forest ecosystems include highly productive old growth with global importance for carbon storage and biodiversity. Current estimates of the remaining amount of “big-treed” old growth vary 10-fold, creating uncertainty that challenges provincial attempts to shift management policy toward ecological integrity. This uncertainty arises from using different remotely sensed indicators and definitions of tree size. No ideal indicator exists. We attempt to improve clarity by evaluating the reliability of candidate indicators, calibrating selected indicators to improve consistency, and generating multiple estimates of the amount of big-treed old growth using calibrated indicators. To evaluate reliability, we compared inventory estimates of tree size and site productivity with measured tree size in 1,945 ground plots. To assess the amount of big-treed old growth, we determined an equivalent “big” size threshold for each indicator and calculated the area of old growth above the size threshold. Stand volume, tree density, basal area, and diameter estimates performed poorly; we selected tree height and two measures of site productivity for further analysis. Estimated tree height best indicated the current old growth size, followed by inventory-based site index and finally ecosystem-based site index. The calibrated indicators agreed that very little remaining old growth supports large trees (1.5–3.3% for the biggest trees; 6–13% including medium-sized trees that represent the largest growing trees in lower productivity interior ecosystems). Tree height cannot be used to compare the remaining area of big-treed old growth to the area expected naturally, an important input for ecological risk assessment and conservation planning because height data are lost from the inventory after harvest. The two calibrated site productivity indicators agreed that the amount remaining is less than 30% of the expected historical amount, posing a high risk to biodiversity and resilience. We recommend using estimated height to identify the biggest remaining old-growth stands for regional planning and calibrated inventory-based site index for risk assessment until a detailed ecosystem mapping has been verified to represent old-growth variability. To reduce uncertainty, we suggest that planning groups compare several indicators and analysis approaches, adjusted to ensure equivalence, and use precaution to avoid any unknowingly increasing risks.

Estimation of individual stem volume and diameter from segmented UAV laser scanning datasets in Pinus taeda L. plantations

ABSTRACT The competitive neighbourhood surrounding an individual tree can have a significant influence on its diameter at breast height (DBH) and individual tree stem volume (SV). Distance dependent competition index metrics are rarely recorded in traditional field campaigns because they are laborious to collect and are spatially limited. Remote sensing data could overcome these limitations while providing estimation of forest attributes over a large area. We used unoccupied aerial vehicle laser scanning data to delineate individual tree crowns (ITCs) and calculated crown size and distance-dependent competition indices to estimate DBH and SV. We contrasted two methods: (i) Random Forest (RF) and (ii) backwards-stepwise, linear multiple regression (LMR). We utilized an existing experiment in Pinus taeda L. plantations including multiple planting densities, genotypes and silvicultural levels. While the tree planting density did affect the correct delineation of ITCs, between 61% and 99% (mean 86%) were correctly linked to the planting location. The most accurate RF and LMR models all included metrics related to ITC size and competitive neighbourhood. The DBH estimates from RF and LMR were similar: RMSE 3.05 and 3.13 cm (R2 0.64 and 0.62), respectively. Estimates of SV from RF were slightly better than for LMR: RMSE 0.06 and 0.07 m3 (R2 0.77 and 0.70), respectively. Our results provide evidence that ITC size and competition index metrics may improve DBH and SV estimation accuracy when analysing laser-scanning data. The ability to provide accurate, and near-complete, forest inventories holds a great deal of potential for forest management planning.

A Shine Muscat Grape Berry Detection and Grape Cluster Compactness Estimation for Assessment of Grape Quality Based on Instance Segmentation Methods

Highlights Mask R-CNN ResNet-101 was analyzed to have the highest detection accuracy for grape berries. Grape cluster compactness is one of the important factors that determines the quality of grapes. Indicator DGC was used to intuitively calculate the compactness of grape cluster. An average error of 1.6 mm occurred with the verification of diameter estimation algorithm. Abstract. Consumption and cultivation of grapes are constantly increasing because they are known as alkaline foods, which relieve fatigue by accelerating carbohydrate metabolism in the human body. Among these grapes, a Shine Muscat doesn’t have the bitter taste of ordinary grapes, has low acidity, and has a crunchy texture, which results in growing interest in modern society. As the consumption of Shine Muscat increases, an automated process to save labor and time becomes essential. Particularly, one challenge that needs to be solved is the determination of grape quality based solely on visual evaluations by experts, which can result in inconsistent pricing for consumers. In this study, an algorithm was adopted to detect the grape berries from a bunch of single grapes by acquiring RGB images of the harvested Shine Muscat. Mask R-CNN, a convolutional neural network-based image segmentation technique, was used with various backbones to compare and evaluate the performance of each model. Results showed that Mask R-CNN ResNet 101 had the highest AP (Average Precision) value of 0.961 among all models. In addition, indexes such as the size (diameter) of each grape berry, the area of the grape on the image, and the area of the empty space that are required to find the compactness of the grape cluster are obtained. In particular, it was analyzed that the average error value (mm) and percent error (%) of the diameter estimation algorithm developed in this study were 1.60 mm and 4.79%, respectively. Keywords: Keywords., Density of Grape Cluster (DGC), Diameter estimation, Grape berry, Grape cluster compactness, Mask R-CNN, Object detection.

Estimation of pore diameters and static H&lt;sub&gt;2&lt;/sub&gt;O capacity of zeolites from IR spectra of absorbed water

Estimation of pore diameters and static H&lt;sub&gt;2&lt;/sub&gt;O capacity of zeolites from IR spectra of absorbed water

Diameter and stem volume estimation based on under canopy UAV-SfM-MVS survey approach in subtropical forest of Okinawa Island, Japan

Diameter and stem volume estimation based on under canopy UAV-SfM-MVS survey approach in subtropical forest of Okinawa Island, Japan

A Novel Method of Situ Measurement Algorithm for Oudemansiella raphanipies Caps Based on YOLO v4 and Distance Filtering

Oudemansiella raphanipies has gradually gained more and more popularity in the market for its delicious taste, while enhancing human immunity and regulating human body functions as well. To achieve the high-throughput and automatic monitoring of the phenotypes of Oudemansiella raphanipies, a novel method, based on YOLO v4 and Distance Filter (DF), was proposed for high-precision diameter estimation of Oudemansiella raphanipies caps. To begin with, a dataset of Oudemansiella raphanipies was established by the laboratory cultivation and collection of factory samples. The improved YOLO v4 target detection model with added CBAM modules to each convolution block in the backbone was trained to locate the caps and, thus, obtain an approximate bounding box. Secondly, the approximate contour of the cap was gained through the H component, canny edge detection operators, and distance filtering to conduct the noise elimination. Finally, the center of the fitted circle and its accurate contour of the cap could be obtained by the constrained least square method, and the diameter of the fitted circle was estimated by the calibration data. The results of practical tests showed that this method achieved an accuracy of 95.36% in recognizing Oudemansiella raphanipies caps in the growing bed, and the fitting effect of caps was superior to Circle Hough Transform (CHT), the least square method (LS), and Ransac, with no manual adjustment on parameters. Compared with the manual measurement, the mean absolute error (MAE) of this method was 0.77 mm, the coefficient of determination (R2) was 0.95, and the root mean square error (RMSE) was 0.96 mm. Therefore, the model had high-cost performance and could meet the needs of continuous and long-term tracking of the cap shape of Oudemansiella raphanipies, providing the basis for future high-throughput breeding and machine picking.

OBSERVATION OF AN OCCULTATION OF THE UCAC4 488-082551 STAR BY ASTEROID (76228) 2000 EH 75 ON 31 MAY 2022

Observations of an occultation of the UCAC4 488-082551 star by asteroid (76228) 2000 EH 75 were carried out on 31 May 2022 at Kryzhanivka observation station of Odesa I. I. Mechnykov National University (the observatory code A85). A set of instruments, which included a Schmidt telescope (the primary mirror diameter D = 271.25 mm; the corrector plate diameter D k = 223.9 mm; the focal length F = 440 mm), a GPS receiver and Videoscan-415-2001 CCD camera, was used to perform observations. The observing conditions were as follows: clear sky, the star’s altitude 49 0 south, the Moon was below the local horizon. The target star from the UCAC4 catalogue (Zacharias et al. 2013) has the following photometric parameters: m B = 14.008; m V = 12.720; m r = 12.284; m i = 11.813. The diameter of the star has not been determined. Asteroid (76228) is a Main Belt asteroid with an orbital period of 4.17178 years. It has an absolute magnitude of 14.93, the visible geometric albedo of 0.123±0.013 and diameter of 5.00±0.23 km (Masiero et al. 2011). The time of occultation predicted using ephemerides was 23:52:44±4 sec. The maximum duration of the occultation was 0.4 sec, provided that the observing site was located at the centre of the occultation track (strip). A drop in the star’s brightness observed during occultation was about 8 m . The occultation event was recorded as a sequence of GIF images with the exposure time of 0.5 sec per frame. The system clock of the computer used for the occultation recording was controlled with a GPS receiver in a fashion similar to that described in the paper by Karbovsky et al. (2017). The data processing yielded an estimate of the occultation duration of 0.46±0.04 sec. The uncertainty of the occultation start time within exposure results in the total estimate of accuracy in timing the maximum phase of occultation 23:52:44.06±0.10 sec. The chord length across the asteroid estimated by timing the occultation is L = 9.2±0.8. This chord length is close to the estimates of the asteroid diameter reported in the paper by Masiero et al. (2011).

Development of a site form equation for predicting and mapping site quality. A case study of unmanaged beech forests in the Cantabrian range (NW Spain)

Site form (SF), expressed as the dominant height of a stand at a reference dominant diameter, is used less often than site index (SI) to estimate forest site quality. However, it has the advantage that it is age-independent and can therefore be applied in a wider set of situations in forestry practice. Like SI, elaboration of SF has traditionally required stem analysis or repeated measurements in permanent plots; however, Airborne Laser Scanning (ALS) can nowadays be used to generate site quality practical maps, thereby simplifying the method. The aims of this study were to fit a dynamic equation to stem analysis data in order estimate SF in natural beech forests in NW Spain, as well as to compare the performance of SF for site quality estimation and to analyze the possibility of using ALS data for site quality prediction in these forests. The Algebraic Difference Approach formulation of the Bertalanffy-Richards model provided the best results and defined four curves for dominant heights of 5, 10, 15 and 20 m at a reference dominant diameter of 20 cm. A significant relationship between SF and SI was observed, and we therefore believe that SF is a good approach for site quality estimation. These results can be used directly at inventory plot level for establishing site quality classes without the need to know the stand age, either in terms of SF or translated into SI. On the other hand, ALS data allowed estimation of both dominant height and dominant diameter, although better results were obtained with the former. Applying the SF dynamic model to both data sets enabled prediction of SF at 25x25 m/pixel (and SI using the SI-SF relationship). The overall accuracy of the relationship between the observed SI and that predicted from ALS metrics yielded a coefficient of determination of 0.456 without bias, heteroscedasticity or absence of normality. The results of this unbiased raster model were considered rather good, as predictions were obtained for a pixel size of 25x25 m (0.0625 ha). Prediction of mean SI value for one hectare would thus be necessary to average the values obtained in 16 pixels with an expected error compensation. Use of the raster model based on ALS metrics will enable site quality estimation for current beech stands at high spatial resolution without the need for fieldwork, providing very valuable information for forest managers and researchers.

Stem Taper Estimation Using Artificial Neural Networks for Nothofagus Trees in Natural Forest

The objective of the study was to estimate the diameter at different stem heights and the tree volume of the Nothofagus obliqua (Mirb.) Oerst., Nothofagus alpine (Poepp. et Endl.) Oerst. and Nothofagus dombeyi (Mirb.) Oerst. trees using artificial neural networks (ANNs) and comparing the results with estimates obtained from six traditional taper functions. A total of 1380 trees were used. The ANN trained to estimate the stem diameter with the best performance generated RMSE values in the training phase of 7.5%, and 7.7% in the validation phase. Regarding taper functions, Kozak’s model generated better RMSE indicators, but performed not as well as that generated by the ANN. The ANN estimation of the total volume was carried out in two phases. The first used the diameter estimation to determine the volume at one-centimeter intervals along the stem (one-phase ANN), and the second used the estimation of the one-phase ANN as an additional variable in an ANN that directly estimated the tree cumulative volume (two-phase ANN). The two-phase ANN method generated the best performance for estimating the cumulative volume in relation to one-phase ANN and the Kozak taper function, generating RMSE values for N. obliqua, N. alpina and N. dombeyi of 9.7%, 8.9% and 8.8%, respectively.

Using Tree Height, Crown Area and Stand-Level Parameters to Estimate Tree Diameter, Volume, and Biomass of Pinus radiata, Eucalyptus globulus and Eucalyptus nitens

Accurate estimates of tree diameter, height, volume, and biomass are important for numerous economic and ecological applications. In this study, we report exponential equations to predict tree DBH (cm), stem volume over bark (VOB, m3), and total above-stump biomass (TASB, kg) using three varying levels of input data for Pinus radiata D. Don, Eucalyptus globulus Labill., and Eucalyptus nitens (H.Deane &amp; Maiden) Maiden planted trees. The three sets of input data included: (1) tree height (HT, m), (2) tree HT and ground projected living crown area (CA, m2), and (3) tree HT, CA, and additional stand parameters. The analysis was performed using a large dataset covering the range of distribution of the species in central Chile and included stands of varying ages and planting densities. The first set of equations using only HT were satisfactory with Adj-R2 values ranging from 0.78 to 0.98 across all species and variables. For all three species, estimation of DBH, VOB, and TASB as a function of HT improved when CA was added as an additional independent variable, increasing Adj-R2 and reducing RMSE. The inclusion of stand variables, such as age and stand density, also resulted in further improvement in model performance. The models reported in this study are a robust alternative for DBH, VOB, and TASB estimations on planted stands across a wide range of ages and densities, when height and CA are known, especially when input data are derived from remote sensing techniques.

A Ground-based Platform for Reliable Estimates of Fruit Number, Size, and Color in Stone Fruit Orchards

Automatic in-field fruit recognition techniques can be used to estimate fruit number, fruit size, fruit skin color, and yield in fruit crops. Fruit color and size represent two of the most important fruit quality parameters in stone fruit (Prunus sp.). This study aimed to evaluate the reliability of a commercial mobile platform, sensors, and artificial intelligence software system for fast estimates of fruit number, fruit size, and fruit skin color in peach (Prunus persica), nectarine (P. persica var. nucipersica), plum (Prunus salicina), and apricot (Prunus armeniaca), and to assess their spatial and temporal variability. An initial calibration was needed to obtain estimates of absolute fruit number per tree and a forecasted yield. However, the technology can also be used to produce fast relative density maps in stone fruit orchards. Fruit number prediction accuracy was ≥90% in all the crops and training systems under study. Overall, predictions of fruit number in two-dimensional training systems were slightly more accurate. Estimates of fruit diameter (FD) and color did not need an initial calibration. The FD predictions had percent standard errors &lt;10% and root mean square error &lt;5 mm under different training systems, row spacing, crops, and fruit position within the canopy. Hue angle, a color attribute previously associated with fruit maturity in peach and nectarine, was the color attribute that was best predicted by the mobile platform. A new color parameter—color development index (CDI), ranging from 0 to 1—was derived from hue angle. The adoption of CDI, which represents the color progression or distance from green, improved the interpretation of color measurements by end-users as opposed to hue angle and generated more robust color estimations in fruit that turn purple when ripe, such as dark plum. Spatial maps of fruit number, FD, and CDI obtained with the mobile platform can be used to inform orchard decisions such as thinning, pruning, spraying, and harvest timing. The importance and application of crop yield and fruit quality real-time assessments and forecasts are discussed.