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The management of individual weights in broiler farming is not only crucial for increasing farm income but also directly linked to the revenue growth of integrated broiler companies, necessitating prompt resolution. This paper proposes a model to estimate daily average broiler weights using time and weight data collected through scales. In the proposed model, a method of self-adjusting weights in the bandwidth calculation formula is employed, and the daily average weight representative value is estimated using KDE. The focus of this study is to contribute to the individual weight management of broilers by intensively researching daily fluctuations in average broiler weight. To address this, weight and time data are collected and preprocessed through scales. The Gaussian kernel density estimation model proposed in this paper aims to estimate the representative value of the daily average weight of a single broiler using statistical estimation methods, allowing for self-adjustment of bandwidth values. When applied to the dataset collected through scales, the proposed Gaussian kernel density estimation model with self-adjustable bandwidth values confirmed that the estimated daily weight did not deviate beyond the error range of ±50 g compared with the actual measured values. The next step of this study is to systematically understand the impact of the broiler environment on weight for sustainable management strategies for broiler demand, derive optimal rearing conditions for each farm by combining location and weight data, and develop a model for predicting daily average weight values. The ultimate goal is to develop an artificial intelligence model suitable for weight management systems by utilizing the estimated daily average weight of a single broiler even in the presence of error data collected from multiple weight measurements, enabling more efficient automatic measurement of broiler weight and supporting both farms and broiler demand. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]