Characterizing prediction errors of a new tree height model for cut-to-length Pinus radiata stems through the Burr TypeⅫdistribution

Unlike height-diameter equations for standing trees commonly used in forest resources modelling,tree height models for cut-to-length(CTL)stems tend to produce prediction errors whose distributions are not conditionally normal but are rather leptokurtic and heavy-tailed.This feature was merely noticed in previous studies but never thoroughly investigated.This study characterized the prediction error distribution of a newly developed such tree height model for Pin us radiata(D.Don)through the three-parameter Burr TypeⅫ(BⅫ)distribution.The model’s prediction errors(ε)exhibited heteroskedasticity conditional mainly on the small end relative diameter of the top log and also on DBH to a minor extent.Structured serial correlations were also present in the data.A total of 14 candidate weighting functions were compared to select the best two for weightingεin order to reduce its conditional heteroskedasticity.The weighted prediction errors(εw)were shifted by a constant to the positive range supported by the BXII distribution.Then the distribution of weighted and shifted prediction errors(εw+)was characterized by the BⅫdistribution using maximum likelihood estimation through 1000 times of repeated random sampling,fitting and goodness-of-fit testing,each time by randomly taking only one observation from each tree to circumvent the potential adverse impact of serial correlation in the data on parameter estimation and inferences.The nonparametric two sample Kolmogorov-Smirnov(KS)goodness-of-fit test and its closely related Kuiper’s(KU)test showed the fitted BⅫdistributions provided a good fit to the highly leptokurtic and heavy-tailed distribution ofε.Random samples generated from the fitted BⅫdistributions ofεw+derived from using the best two weighting functions,when back-shifted and unweighted,exhibited distributions that were,in about97 and 95%of the 1000 cases respectively,not statistically different from the distribution ofε.Our results for cut-tolength P.radiata stems represented the first case of any tree species where a non-normal error distribution in tree height prediction was described by an underlying probability distribution.The fitted BXII prediction error distribution will help to unlock the full potential of the new tree height model in forest resources modelling of P.radiata plantations,particularly when uncertainty assessments,statistical inferences and error propagations are needed in research and practical applications through harvester data analytics.

Machine Vision Based Fish Cutting Point Prediction for Target Weight

Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.

Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model

Food choice motives(i.e.,mood,health,natural content,convenience,sensory appeal,price,familiarities,ethical concerns,and weight control)have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world.Researchers from several domains have presented several models addressing issues influencing food choice over the years.However,a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure.In this paper,four Deep Learning(DL)models and one Machine Learning(ML)model are utilized to predict the weight in pounds based on food choices.The Long Short-Term Memory(LSTM)model,stacked-LSTM model,Conventional Neural Network(CNN)model,and CNN-LSTM model are the used deep learning models.While the applied ML model is the K-Nearest Neighbor(KNN)regressor.The efficiency of the proposed model was determined based on the error rate obtained from the experimental results.The findings indicated that Mean Absolute Error(MAE)is 0.0087,the Mean Square Error(MSE)is 0.00011,the Median Absolute Error(MedAE)is 0.006,the Root Mean Square Error(RMSE)is 0.011,and the Mean Absolute Percentage Error(MAPE)is 21.Therefore,the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM,CNN,CNN-LSTM,and KNN regressor.

Structural Traits,Structural Indices and Body Weight Prediction of Arsi Cows

Structural measurements are indicators of animal performance,productivity and carcass characteristics.This study was conducted with the objectives of assessing structural measurements,developing body weight prediction and structural indices for cows of Arsi breed.The cows were purchased from highland and lowland agro-ecologies of Arsi and East Shoa zones of Oromia regional state,Ethiopia and kept in Adami Tulu Agricultural Research Center(ATARC)for the breed development purpose.Totally 222 cows were included in the structural traits measurement.Thirty four young heifers were also considered in the study.Twenty two structural traits were considered during observational survey.The structural index was calculated from the phenotypically correlated linear measurements.Structural traits were analyzed by T-test of SPSS version twenty four.The observed average values of height at wither,chest depth,heart girth,body length,pelvic width,cannon bone circumferences of the cows were 107,55.62,141.06,117.82,31.41 and 13.58cm,respectively.Heart girth(0.82),flank girth(0.73),hook circumferences(0.67),chest depth(0.65)and height at rump(0.64)were highly correlated(P<0.01)to body weight of the cows.Regression analysis indicated that hearth girth had the highest coefficient of determination for body weight of the cows and heifers.Accordingly,the simple linear equations were developed to predict the body weight of cows and heifers.Body weight of Arsi cow(y)=-221.005+3.1(heart girth)and Body weight of Arsi heifer(y)=-188.452+2.75(heart girth).Based on this,the measuring chart tape can be developed to estimate the body weight of Arsi cows and heifers at field condition where there is no access to weighing scales.

Predicting fetal weight by three-dimensional limb volume ultrasound (AVol/TVol) and abdominal circumference

Background:Fetal weight is an important parameter to ensure maternal and child safety.The purpose of this study was to use three-dimensional(3D)limb volume ultrasound combined with fetal abdominal circumference(AC)measurement to establish a model to predict fetal weight and evaluate its efficiency.Methods:A total of 211 participants with single pregnancy(28-42 weeks)were selected between September 2017 and December 2018 in the Beijing Obstetrics and Gynecology Hospital of Capital Medical University.The upper arm(AVol)/thigh volume(TVol)of fetuses was measured by the 3D limb volume technique.Fetal AC was measured by two-dimensional ultrasound.Nine cases were excluded due to incomplete information or the interval between examination and delivery>7 days.The enrolled 202 participants were divided into a model group(134 cases,70%)and a verification group(68 cases,30%)by mechanical sampling method.The linear relationship between limb volume and fetal weight was evaluated using Pearson Chi-squared test.The prediction model formula was established by multivariate regression with data from the model group.Accuracy of the model formula was evaluated with verification group data and compared with traditional formulas(Hadlock,Lee2009,and INTERGROWTH-21st)by paired t-test and residual analysis.Receiver operating characteristic curves were generated to predict macrosomia.Results:AC,AVol,and TVol were linearly related to fetal weight.Pearson correlation coefficient was 0.866,0.862,and 0.910,respectively.The prediction model based on AVol/TVol and AC was established as follows:Y=-481.965+12.194TVol+15.358AVol+67.998AC,R2adj=0.868.The scatter plot showed that when birth weight fluctuated by 5%(i.e.,95%to 105%),the difference between the predicted fetal weight by the model and the actual weight was small.A paired t-test showed that there was no significant difference between the predicted fetal weight and the actual birth weight(t=-1.015,P=0.314).Moreover,the residual analysis showed that the model formula’s prediction efficiency was better than the traditional formulas with a mean residual of 35,360.170.The combined model of AVol/TVol and AC was superior to the Lee2009 and INTERGROWTH-21st formulas in the diagnosis of macrosomia.Its predictive sensitivity and specificity were 87.5%and 91.7%,respectively.Conclusion:Fetal weight prediction model established by semi-automatic 3D limb volume combined with AC is of high accuracy,sensitivity,and specificity.The prediction model formula shows higher predictive efficiency,especially for the diagnosis of macrosomia.