Carbon/Nitrogen Ratio Estimation for Urban Organic Waste Using Convolutional Neural Networks

Authors

  • Andrea de Anda-Trasviña Centro de Investigaciones Biológicas del Noroeste
  • Alejandra Nieto-Garibay Centro de Investigaciones Biológicas del Noroeste
  • Fernando D. Von Borstel Centro de Investigaciones Biológicas del Noroeste
  • Enrique Troyo-Diéguez Centro de Investigaciones Biológicas del Noroeste
  • José Luis García-Hernández Universidad Juárez del Estado de Durango
  • Joaquin Gutierrez Centro de Investigaciones Biológicas del Noroeste

DOI:

https://doi.org/10.13053/cys-27-3-4301

Keywords:

Fruit waste, Carbon/Nitrogen ratio, composting, convolutional neural network, image processing

Abstract

In this paper, the Carbon/Nitrogen ratio was estimated by classifying the urban organic waste (UOW) based on qualitative (color and maturity) and quantitative (weight) characteristics via convolutional neural networks (CNN) and image processing. The reuse of UOW is a suitable process in waste management, preventing its disposition in landfills and reducing the effects on the environment and human health. Ambient conditions affect the UOW characteristics over time. Knowing these changes is essential to reuse them appropriately, mainly both carbon and nitrogen content. A categorization associated with the decomposition stage of the UOW was proposed, which becomes the corresponding UOW classes. Three convolutional neural network models were trained with UOW images. Two pre-trained CNN (MobileNet and VGG16) were trained by transfer learning technique, and one proposed model (UOWNet) was trained from scratch. The UOWNet model presented a good agreement for the classification task. The results show that this preprocess is a practical tool for assessing the Carbon/Nitrogen ratio of UOW from its qualitative and quantitative features through image analysis. It is a preliminary framework aimed to support household organic waste recycling and community sustainability.

Author Biographies

Andrea de Anda-Trasviña, Centro de Investigaciones Biológicas del Noroeste

She is a PhD student in Sciences at the Centro de Investigaciones Biológicas del Noroeste (CIBNOR) in La Paz, BCS, Mexico. Her current research interests focus on machine learning algorithms for biological applications.

Alejandra Nieto-Garibay, Centro de Investigaciones Biológicas del Noroeste

Titular researcher and coordinator of Arid-Zone Agriculture at CIBNOR S.C.

Fernando D. Von Borstel, Centro de Investigaciones Biológicas del Noroeste

Since December 1995, he has worked at the Centro de Investigaciones Biológicas del Noroeste (CIBNOR) in La Paz, BCS, Mexico. His research interest includes the robotic systems model-based development, computer vision, and artificial intelligence applications.

Enrique Troyo-Diéguez, Centro de Investigaciones Biológicas del Noroeste

He is a Titular Researcher in Arid-Zone Agriculture at CIBNOR S.C.

José Luis García-Hernández, Universidad Juárez del Estado de Durango

He is a Professor in Universidad Juárez del Estado de Durango-Facultad de Agricultu

Joaquin Gutierrez, Centro de Investigaciones Biológicas del Noroeste

He is a Researcher with the Engineering Group of the Centro de Investigaciones Biológicas del Noroeste, S.C., La Paz, BCS, México. His current research interests include the development and experimental validation of robotic systems for biological research applications.

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Published

2023-09-26

Issue

Vol. 27 No. 3 (2023): 27(3) 2023

Section

Articles

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