The role of pig size prediction in supply chain planning

Apichottanakul, Arthit; Pathumnakul, Supachai; Piewthongngam, Kullapapruk

doi:10.1016/j.biosystemseng.2012.07.008

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Artificial neural networks (ANNs) have been widely employed in establishing mathematical models for complex relationships. They possess significant advantages in handling fuzzy, stochastic, and nonlinear data [13][14][15]. In predicting and optimizing agricultural equipment parameters, ANNs also show great promise.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Mechanical Properties of Root–Substrate Interaction with a Transplanter Using Artificial Neural Networks

Tian,

Gao,

et al. 2024

Agriculture

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The mechanical properties of a plug seedling substrate determine whether it will crush during the transplantation, thereby affecting the integrity of the root system and the survival rate of transplanted seedlings. In this study, we measured eight morphological parameters of pepper seedlings using machine vision and physical methods, and the corresponding substrate mechanical parameters of the plug seedlings were tested using a texture analyzer. Based on the experimental data, a BPNN framework was constructed to predict the substrate mechanical properties of plug seedlings at different growth stages. The results indicate that the BPNN with a framework of [8, 15, 15, 1] exhibits higher R2 and lower errors. The mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error (MAPE) values are 7.669, 88.842, and 9.076%, respectively, with an R2 of 0.867. The average prediction accuracy of 20 test data set is 90.472%. Finally, predictions and experimental validations were conducted on the substrate mechanical properties of seedlings grown for 47 days. The results revealed that the BPNN achieved an average prediction accuracy of 93.282%. Additionally, it exhibited faster speed and lower computational costs. This study provides a reference for the non-intrusive estimation of substrate mechanical properties in plug seedlings and the design and optimization of transplanting an end-effector.

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Section: Introductionmentioning

confidence: 99%

Modeling the Mechanical Properties of Root–Substrate Interaction with a Transplanter Using Artificial Neural Networks

Tian,

Gao,

et al. 2024

Agriculture

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show abstract

“…Y. Wang, W. Yang, P. Winter, and L. Walker [3] created a machine vision to determine pig liveweight based on pig image using NN. A. Apichottanakul, S. Pathumnakul, and K. Piewthongngam [4] applied NN to measure the average weight of pigs. S. K. Biswas, B. Baruah, B. Purkayastha, and M. Chakraborty [5] used NN for swine flu diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Modified Flower Pollination Algorithm for Disease Identification in Swine

2021

IJIES

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“…In swine supply chain management, matching suitable size pigs to the food products during the slaughtering and food processing stages could improve the production efficiency by reducing the raw material procurement, inventory, shortage and perished costs. The ability to accurately estimate pig sizes before setting up harvesting or procurement plans is very crucial for the industry (Apichottanakul et al, 2012).…”

Section: Introductionmentioning

confidence: 99%