Method for C/N ratio estimation using Mask R-CNN and a depth camera for organic fraction of municipal solid wastes

Jingjing Huang,Heming Hu,Hongduo Zhang,Liang Zhang,Xu Xiao,Ming Li,Yongwei Xu,Jingqi Huang,Jingxin Xie,Sihui Dai

doi:10.25165/j.ijabe.20211405.6382

Jingjing Huang, Heming Hu + Show 8 more

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https://doi.org/10.25165/j.ijabe.20211405.6382

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Abstract

Fast assessment of the initial carbon to nitrogen ratio (C/N) of organic fraction of municipal solid waste (OFMSW) is an important prerequisite for automatic composting control to improve efficiency and stability of the bioconversion process. In this study, a novel approach was proposed to estimate the C/N of OFMSW, where an instance segmentation model was applied to predict the masks for the waste images. Then, by combining the instance segmentation model with the depth-camera-based volume calculation algorithm, the volumes occupied by each type of waste were obtained, therefore the C/N could be estimated based on the properties of each type of waste. First, an instance segmentation dataset including three common classes of OFMSW was built to train mask region-based convolutional neural networks (Mask R-CNN) model. Second, a volume measurement algorithm was proposed, where the measurement result of the object was derived by accumulating the volumes of small rectangular cuboids whose bottom area was calculated with the projection property. Then the calculated volume was corrected with linear regression models. The results showed that the trained instance segmentation model performed well with average precision scores AP50 = 82.9, AP75 = 72.5, and mask intersection over unit (Mask IoU) = 45.1. A high correlation was found between the estimated C/N and the ground truth with a coefficient of determination R2=0.97 and root mean square error RMSE = 0.10. The relative average error was 0.42% and the maximum error was only 1.71%, which indicated this approach has potential for practical applications. Keywords: carbon to nitrogen ratio, estimation, volume measurement, organic fraction of municipal solid waste, depth camera, instance segmentation DOI: 10.25165/j.ijabe.20211405.6382 Citation: Huang J J, Zhang H D, Xiao X, Huang J Q, Xie J X, Zhang L, et al. Method for C/N ratio estimation using Mask R-CNN and a depth camera for organic fraction of municipal solid wastes. Int J Agric & Biol Eng, 2021; 14(5): 222–229.

Highlights

Urbanization and rapid growth in human population have resulted in generation of vast amounts of wastes, where the degradable waste, i.e., organic fraction of municipal solid waste (OFMSW), normally accounts for more than half of the total
During the transformation of organic waste in the composting process, carbon and nitrogen are the most vital nutrients for microorganisms to build cell structures and acquire metabolic energy[10], and the initial Carbon to Nitrogen ratio (C/N) of feedstock is one of the crucial factor influencing degradation rate and nutrients losses, which can be adjusted with different bulking agent[11,12,13]
Althaus et al.[16] proposed an approach based on near-infrared reflectance spectroscopy (NIRS) for estimation of nitrogen and carbon content in animal feces with a high coefficient of determination (R2) of 0.97

Summary

Introduction

Urbanization and rapid growth in human population have resulted in generation of vast amounts of wastes, where the degradable waste, i.e., organic fraction of municipal solid waste (OFMSW), normally accounts for more than half of the total amount[1,2]. During the transformation of organic waste in the composting process, carbon and nitrogen are the most vital nutrients for microorganisms to build cell structures and acquire metabolic energy[10], and the initial Carbon to Nitrogen ratio (C/N) of feedstock is one of the crucial factor influencing degradation rate and nutrients losses, which can be adjusted with different bulking agent[11,12,13]. To meet the prerequisite of adjusting C/N, analytical technique such as elemental analyzers is often used in many studies to determine carbon content and total nitrogen of the feedstock[14,15]; it is accurate but too complicated to be performed automatically. Althaus et al.[16] proposed an approach based on near-infrared reflectance spectroscopy (NIRS) for estimation of nitrogen and carbon content in animal feces with a high coefficient of determination (R2) of 0.97.

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