PREVISÃO DE MEDIDA DE PESO BOVINO UTILIZANDO REDES NEURAIS CONVOLUCIONAIS

This study presents a solution for predicting weight measurements in cattle through machine learning techniques using images. Weighing cattle is crucial for optimizing production and ensuring animal welfare, but the traditional method can cause stress to the animal. The aim is to develop a model based on convolutional neural networks to estimate the weight of cattle through image analysis, minimizing the need for direct handling. The main contributions include the proposal of a convolutional neural network model, the comparison of different image filters (RGB and Grayscale), and the analysis of various perspectives of the cattle (lateral and frontal). The expected results seek to validate the efficiency of the proposed model and contribute to more sustainable and efficient practices in livestock farming.

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Publication Details

Journal
Revista Interface Tecnológica
Published
2025-09-17
DOI
https://doi.org/10.31510/infa.v21i2.2044
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

PREVISÃO DE MEDIDA DE PESO BOVINO UTILIZANDO REDES NEURAIS CONVOLUCIONAIS

Cirênio de Almeida Barbosa, Renan Guilherme Nespolo, José Lucas Gomes, Paulo Eduardo Cardoso Andrade
Revista Interface Tecnológica
Smart Agriculture and AI
article

PREVISÃO DE MEDIDA DE PESO BOVINO UTILIZANDO REDES NEURAIS CONVOLUCIONAIS

Cirênio de Almeida Barbosa, Renan Guilherme Nespolo, José Lucas Gomes, Paulo Eduardo Cardoso Andrade
article en

Abstract

This study presents a solution for predicting weight measurements in cattle through machine learning techniques using images. Weighing cattle is crucial for optimizing production and ensuring animal welfare, but the traditional method can cause stress to the animal. The aim is to develop a model based on convolutional neural networks to estimate the weight of cattle through image analysis, minimizing the need for direct handling. The main contributions include the proposal of a convolutional neural network model, the comparison of different image filters (RGB and Grayscale), and the analysis of various perspectives of the cattle (lateral and frontal). The expected results seek to validate the efficiency of the proposed model and contribute to more sustainable and efficient practices in livestock farming.

Revista Interface TecnológicaVol. 21(2)
São Paulo State Technological College (BR), Caribbean University (PR)
Openalex Percentile: Top 82%
Smart Agriculture and AI
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