The Estimating of Rambutan's Peel Thickness by Using the Artificial Neural Network for Designing of the Rambutan Peeling Machine

Saengswarng, Supachoke; Thangdee, S.

doi:10.1109/icitm.2019.8710721

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The optimal ANN model for this case consists of one hidden layer with six neurons in the hidden layer, capable of producing a value of rambutan skin thickness with a curve R2 value between the predicted value of the optimal ANN and the actual value of 0.9401. And these results will be transformed into an equation for the relationship between the maximum diameter on the Z-axis and the skin thickness on the Z-axis to be applied to the rambutan peeler in subsequent work [65].…”

Section: The Use Of Artificial Neural Network In Agricultural Productsmentioning

confidence: 99%

The Use of Artificial Neural Networks in Agricultural Plants

Susanti

Nofendra

Zaini

et al. 2023

AJEEET

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Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.

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Section: The Use Of Artificial Neural Network In Agricultural Productsmentioning

confidence: 99%