Abstract
Determining the slaughter weight of pigs is crucial to the profitability of swine production farms. Unfortunately, in develo** countries, the basic infrastructure for weight measurement may not always be available, affecting farmers’ income. This study presents a machine learning-based approach to determine the dressed weight of pigs using four morphometric dimensions: paunch girth (PG), heart girth (HG), body length and wither height, which can be measured in situ. Different neural network model structures were constructed taking LM, GDX and BR training algorithms, tansigmoid/logsigmoid hidden layer transfer functions and 5–30 hidden layer neurons (HLNs). Results showed that LM training algorithm with logsigmoidal transfer function and 20 HLNs resulted in 99.8% accuracy in determining the pig dressed weight. Further, the number of morphometric parameters as inputs was gradually reduced and it was found that 99% accuracy can still be achieved using just PG and HG, thereby reducing the measurement time.
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The authors thank ICAR-IVRI for providing necessary funds to carry out this research work under office order 3-8/2022-23/Budget.
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SB carried out the experiments, did formal analysis and wrote the manuscript; GKG and AT conceptualized and supervised the work, reviewed and edited the manuscript and validated the results.
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Bhoj, S., Gaur, G.K. & Tarafdar, A. An intelligent model for predicting the dressed weight of pigs using morphometric measurements. J Food Sci Technol 60, 1841–1845 (2023). https://doi.org/10.1007/s13197-023-05704-4
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DOI: https://doi.org/10.1007/s13197-023-05704-4