Image processing strategies for pig liveweight measurement: Updates and challenges

Bhoj, Suvarna; Tarafdar, Ayon; Chauhan, Anuj; Singh, Mukesh; Gaur, Gyanendra Kumar

doi:10.1016/j.compag.2022.106693

Cited by 20 publications

(16 citation statements)

References 88 publications

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“…Después de la adquisición de imágenes se realizan varios pasos de procesamiento como filtrado, extracción de características, y el entrenamiento y la formación de bases de datos para un pronóstico confiable de PV. (Bhoj et al, 2022). (ver Figura 4) Figura 4: Pasos básicos de procesamiento de imágenes para predecir el peso vivo de los cerdos.…”

Section: Estado Del Arteunclassified

Aprendizaje Automático aplicado a la calidad del desarrollo en la ganadería de precisión

Aranda

Araguás²,

Redolf³

2022

AJEA

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La ganadería de precisión trata de incorporar a los procesos ganaderos distintos sistemas, herramientas y tecnologías digitales para ayudar a tomar decisiones más precisas. En el presente trabajo se realiza la descripción y el estado del arte a través de aprendizaje automático aplicados a la ganadería de precisión, además se describen las actividades y metodologías propuestas a través del aprendizaje automático aplicado a la ganadería de precisión a los efectos de mejorar la producción y rentabilidad, con sostenibilidad en el territorio nacional para el monitoreo y control de animales en sus diferentes etapas (cría, engorde o ciclo completo).

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Section: Estado Del Arteunclassified

Aprendizaje Automático aplicado a la calidad del desarrollo en la ganadería de precisión

Aranda

Araguás²,

Redolf³

2022

AJEA

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“…In the mid-nineties, Brandl et al [ 14 ] analyzed pigs’ body areas from digital images to estimate their weights, and were able to predict with less than 6% deviation [ 14 ]. Although digital image processing has shown great results, it comes with challenges, such as pigs needing to be under the camera at a somewhat predetermined position, color patterns or dirt on the animals, and lighting conditions needing to be consistent [ 6 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models to Predict Finishing Pig Weight Using Point Clouds

Paudel,

de Sousa,

Sharma

et al. 2023

Animals

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The selection of animals to be marketed is largely completed by their visual assessment, solely relying on the skill level of the animal caretaker. Real-time monitoring of the weight of farm animals would provide important information for not only marketing, but also for the assessment of health and well-being issues. The objective of this study was to develop and evaluate a method based on 3D Convolutional Neural Network to predict weight from point clouds. Intel Real Sense D435 stereo depth camera placed at 2.7 m height was used to capture the 3D videos of a single finishing pig freely walking in a holding pen ranging in weight between 20–120 kg. The animal weight and 3D videos were collected from 249 Landrace × Large White pigs in farm facilities of the FZEA-USP (Faculty of Animal Science and Food Engineering, University of Sao Paulo) between 5 August and 9 November 2021. Point clouds were manually extracted from the recorded 3D video and applied for modeling. A total of 1186 point clouds were used for model training and validating using PointNet framework in Python with a 9:1 split and 112 randomly selected point clouds were reserved for testing. The volume between the body surface points and a constant plane resembling the ground was calculated and correlated with weight to make a comparison with results from the PointNet method. The coefficient of determination (R2 = 0.94) was achieved with PointNet regression model on test point clouds compared to the coefficient of determination (R2 = 0.76) achieved from the volume of the same animal. The validation RMSE of the model was 6.79 kg with a test RMSE of 6.88 kg. Further, to analyze model performance based on weight range the pigs were divided into three different weight ranges: below 55 kg, between 55 and 90 kg, and above 90 kg. For different weight groups, pigs weighing below 55 kg were best predicted with the model. The results clearly showed that 3D deep learning on point sets has a good potential for accurate weight prediction even with a limited training dataset. Therefore, this study confirms the usability of 3D deep learning on point sets for farm animals’ weight prediction, while a larger data set needs to be used to ensure the most accurate predictions.

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“…To achieve this, several modelling and optimization strategies have been investigated over the past decade. Among these techniques, machine learning and evolutionary algorithms have gained tremendous popularity due to their robustness and wide applicability in animal science [4,5].…”

Section: Introductionmentioning

confidence: 99%

Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security

Bhardwaj

Tarafdar

Baghel

et al. 2023

Journal of Food Quality

Self Cite

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Artificial neural networks (ANNs) in conjugation with genetic algorithms (GAs) have been demonstrated to be an effective tool for system modelling and optimization in a variety of applications. The current communique is about assessing the capacity of ANN to predict investment on cattle till age at first calving (AFC) and milk production based on the data of 340 Vrindavani crossbreed cattle developed at the ICAR-Indian Veterinary Research Institute in Izatnagar, India. Three distinct artificial neural network (ANN) algorithms, namely, Levenberg–Marquardt (LM), Bayesian regularization (BR), and gradient descent momentum with adaptive learning rate backpropagation (GDX) were used to train the ANN infrastructure for determining milk production and investment based on body weight and AFC as input variables. The results showed that BR with 2 hidden layer neurons showed excellent prediction ability (R2 = 0.999, MSE < 10−6) and was therefore used as an objective function by GA for optimization. The optimized results revealed that higher milk production is achievable at lower investment if the age at first calving is 768 days with a body weight of ∼281 kg. The information generated by this investigation will aid in ensuring food security in terms of higher milk production while making the dairy business more sustainable and profitable for the farmers.

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Image processing strategies for pig liveweight measurement: Updates and challenges

Cited by 20 publications

References 88 publications

Aprendizaje Automático aplicado a la calidad del desarrollo en la ganadería de precisión

Aprendizaje Automático aplicado a la calidad del desarrollo en la ganadería de precisión

Deep Learning Models to Predict Finishing Pig Weight Using Point Clouds

Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security

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