## Abstract

A non-invasive automatic broiler weight estimation and prediction method based on a machine learning algorithm was developed to address the issue of high labor costs and stress responses caused by the traditional broiler weighing method in large-scale broiler production. Machine learning algorithms are a data-driven strategy that enables computer systems to make predictions and judgments based on patterns and regularities that they have learned. To estimate the current weight of individual live broilers on farms, machine learning algorithms such as the Gaussian mixture model, Isolation Forest, and Ordering Points To Identify the Clustering Structure (OPTICS) are used to filter and extract data features using a two-stage clustering and noise reduction process. Real-time weight prediction was also achieved by combining polynomial fitting and the gray models and adjusting the model parameters based on prediction accuracy feedback. The symmetric mean absolute percentage error (SMAPE) value is a metric that is commonly used to evaluate the predictive performance of a model by comparing the degree of error between the model’s predicted value on the day of slaughter and the true value measured manually, and the results of the experiments on 111 datasets showed that 7.21% were less than or equal to 0.03, 28.83% were less than or equal to 0.1 and greater than 0.03, and 31.53% were less than or equal to 0.2 and greater than 0.1. This method can be used as a prediction scheme for broiler weight monitoring in a large-scale rearing environment, considering the cost of implementation and the accuracy of estimation.

Keywords:

machine learning; multilevel clustering; broiler weight estimation; weight prediction; GMM; OPTICS

## 1. Introduction

Broilers’ body weight is an important indicator of their health, and effective broiler body weight monitoring is a problem that must be solved in the process of large-scale broiler farming. Meanwhile, large poultry companies typically cover a vertical chain from breeding farms, chicken farms, slaughterhouses, and distributors to form a complete broiler supply chain in order to maximize profits. Due to the lack of a younger generation of workers willing to work in the broiler industry, companies in Korea contract with hundreds of farms to meet market demand for broilers with acceptable specifications, and to meet the production capacity of their slaughterhouses. Because of the large price difference between qualified and substandard broilers, agribusinesses must increase the qualification rate, i.e., broiler production within a specified weight and size range, to avoid potential profit losses. Farmers, on the other hand, can earn additional incentive gains based on the number of broilers that meet the weight standards. As a result, on-farm real-time monitoring of live broiler weights and slaughter time control are critical for revenue management.

Broiler body weight estimation and monitoring have been extensively studied and can be categorized into four main directions:

- Traditional growth curves and growth models based on fitting mathematical functions. For example, Topal et al. [1] studied the fitting and prediction of avian weight–age relationships and compared the goodness of fit between MMF, Weibull, logistic, Gompertz, and von Bertalanffy models. Moharrery et al. [2] proposed a methodology to study and predict the growth characteristics of commercial broilers and indigenous chickens using a nonlinear function, and they used several statistical methods to evaluate the fit of the function and differences in growth parameters. Rizzi et al. [3] investigated growth patterns and sex differences in poultry meat production by comparing different models, such as linear, logistic, Gompertz, and Richards models, and the effect of fit analysis revealed a flexible growth function. Mouffok et al. [4], for the Cobb500 strain of meat birds, found that the Gompertz model was more accurate in estimating body weights in the early stages when comparing and evaluating the fit and predictive effects of different models.
- Live weight estimation methods based on digital image processing. For example, Wet et al. [5] proposed a method to analyze images of broilers using commercial software and established a nonlinear regression equation to estimate the body weight of broilers by statistically analyzing the nonlinear relationship between their surface area, girth, and body weight, which was found to be less accurate compared to image analysis of pigs. Chedad et al. [6] proposed a method to estimate the body weight of chickens by image analysis, and the results of the study showed that the results of automated weighing systems tend to underestimate the actual body weight of chickens at the end stage of the growth period. Bazlur et al. [7] proposed a method to develop a linear equation for estimating the body weight of broilers by analyzing the digital images of their body surface area, validated using a random sample of 100 broilers, and the highest error between the manually measured weight and estimated weight was 16.47%, while the lowest error was 0.04%. Mortensen et al. [8] proposed a method for predicting the weight of broilers based on a 3D camera and image processing algorithms, where the average relative error between the predicted and true weight on the test dataset was 7.8%, and as the density of chickens increased, the absolute error of prediction became larger in the later stages of breeding. Amraei et al. [9] proposed a research method that includes the use of machine vision techniques to extract features related to body weight and the use of artificial neural network algorithms for predicting body weight, with prediction errors mainly centered on less than 50 g.
- Body weight monitoring methods based on audio analysis. For example, Aydin et al. [10] conducted a study to determine the feed intake of chickens by detecting the birds’ pecks and comparing them with feed intake measured by a weighing system. They discovered a linear correlation between the number of pecks and feed intake, with 93% of pecks being accurately identified. Fontana et al. [11] developed a tool that can automatically detect the growth status of broiler chickens at varying ages based on the frequency of calls emitted by the chickens. The results of the statistical analysis showed a significant correlation between the age and weight of the chickens and the maximum power frequency (PF) emitted in their calls. Fontana et al. [12] applied SAS 9.3 software programs, including PROC TTEST, PROC CORR, and PROC REG, to perform regression analyses and statistical tests. Statistical and regression analyses indicated a notable correlation between the sound frequency, age, and body weight of broilers. Fontana et al. [13] conducted a study on the use of sound analysis to predict the body weight of broilers and found a considerable correlation between age and body weight. Incidentally, they established that frequency analyses of chickens’ crowing may be disrupted by filters and ambient noise during the final stages of broiler growth. The study revealed that filters and environmental noise during the final stages of broiler growth may interfere with the frequency analysis of chicken calls. Abdel-Kafy et al. [14] utilized statistical analysis software and regression modeling to predict the body weight of turkeys by recording their vocalizations and corresponding body weights. The results demonstrated a decrease in the frequency of vocalizations with age.
- Direct predictive modeling based on other sensor data (nutritional intake, ventilation, temperature, humidity, etc.) or weight data. For example, Johansena et al. [15] proposed a research methodology to predict broiler weight utilizing a dynamic neural network model. The model was trained using an LM optimization algorithm, with input variables selected based on mutual information. Additionally, kernel density estimation was employed to estimate the joint probability density function. The system achieved an average root-mean-square error of prediction of 66.8 g. Lee et al. [16] developed an automated chicken weighing system composed of weighing scales and workstations. The weighing scale was built using an aluminum plate and a 5 kg load cell, and weight data were transmitted wirelessly to the workstation via a transmission module. The workstation collects data every 15 s and compares the average weight per day with a reference value to monitor the growth and development of the chickens. Weihong Ma et al. [17] introduced an effective method for extracting values using dynamic weighing. Their approach involves an improved amplitude-limited filtering algorithm and a BP neural network model to analyze data such as age, daily weight gain, average speed, and preprocessed weight values. The weighing error was reduced from 6% to less than 3% through a data-driven framework proposed by Chunyao Wang et al. [18] This framework employs Gaussian mixture modeling, self-sampling, and weighted averaging techniques to enhance the accuracy of monitoring and predicting live chicken weights. Birzniece et al. [19] suggested utilizing a long short-term memory (LSTM) artificial neural network for broiler weight prediction, based on environmental factors including temperature, gas concentration, humidity, broiler weight, and feed consumption.

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