Prediction of the fruit development stage of sweet pepper (Capsicum annum var. annuum) by an ensemble model of convolutional and multilayer perceptron-Reference-Cited by-同舟云学术

Prediction of the fruit development stage of sweet pepper (Capsicum annum var. annuum) by an ensemble model of convolutional and multilayer perceptron

Published:2021-10 Issue: Volume:210 Page:171-180
ISSN:1537-5110
Container-title:Biosystems Engineering
language:en
Short-container-title:Biosystems Engineering

Author:

Moon Taewon^ORCID,Park Junyoung,Son Jung Eek^ORCID

Publisher

Elsevier BV

Subject

Soil Science,Agronomy and Crop Science,Food Science,Control and Systems Engineering

Reference45 articles.

1. TensorFlow: Large-scale machine learning on heterogeneous distributed systems;Abadi,2016

2. Long-term yield prediction of greenhouse sweet pepper crops;Al-Halimi;GSTF Journal on Agricultural Engineering,2015

3. Mathematical modeling in the estimation of pepper (Capsicum annuum L.) fruit volume;Bozokalfa;Chilean Journal of Agricultural Research,2010

4. Manual for the preparation and sale of fruits and vegetables : From field to market;Camelo;Food & Agriculture Organization,2004

5. Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system;Cárdenas-Pérez;Biosystems Engineering,2017

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