A fast and easy method for predicting agricultural waste compost maturity by image-based deep learning

Xue, Wei; Hu, Xuejiao; Zhong, Wei; Mei, Xinlan; Chen, Xingjian; Xu, Yangchun

doi:10.1016/j.biortech.2019.121761

Cited by 47 publications

(17 citation statements)

References 19 publications

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“…Automatically segment apples from images and predicts the yield [195] Photographs CNN Regression Predicting agricultural waste (vegetable, livestock, and straw) [196] Photographs CNN ODR f ) Classification Object detections and facial recognition [54,55] Satellite images CNN Classification Detection and classification of oil spills found in open oceans [197] Photographs CNN Classification Classification of civil structural damage types (crack, corrosion, and rust) [198] Video CNN Classification Real-time classification of pedestrians [199] TEM Magnetic resonate imaging; b) Medical image diagnosis and analysis; c) Ultrasound image; d) Computed tomography; e) Precision agriculture; f ) Object detection and recognition; g) Transmission electron microscopy images; h) biomedical image diagnosis and analysis; i) FM images; j) Hematoxylin and eosin staining microscope images; k)…”

Section: Cnn Segmentation Regressionmentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

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Section: Cnn Segmentation Regressionmentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

124

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“…Some researches related to composting, liquid organic fertilizers, and supporting equipment made from agricultural waste has been conducted. Xue et al [12] conducted a study related to predicting the compost's maturity from fermented agricultural waste through the dynamic trough composting method. Besides, Wu et al [13] have also tested the addition of Zeolite and Biochar during composting of agricultural waste.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of the Macronutrient Content of Compost and Liquid Organic Fertilizer from Agricultural Wastes

Devianti¹,

Satriyo²,

Bulan³

et al. 2021

IJDNE

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Agricultural products have great potential to produce untapped farm-to-table agricultural waste. This can happen because the agricultural products are damaged before they reach consumers and become agricultural waste. Therefore, paper aims to investigate the macronutrient content of the compost and liquid organic fertilizer using agricultural waste as the main ingredient. There are two treatments for making compost, namely the composition of the main ingredients (rice straw + lamtoro, rice straw + corn stalks, and lamtoro + corn stalks) and the composition of the supporting material in the form of livestock manure (cow dung, goat manure, and chicken manure). There are five treatment sources of the main raw materials for making liquid organic fertilizer tested, namely banana peel, papaya peel, pineapple skin, tomato, and cassava peel. Macro parameters in the form of N, P, K, and C content were measured using the Kjeldahl, Bray, AAS, and Walkle and Black methods, respectively. The C/N ratio was calculated by comparing the content of C and N. Furthermore, data were analyzed using statistical parameters in the form of ANOVA and DMRT. Making compost with the main ingredients of agricultural waste and supporting materials from livestock manure has a significant effect on macronutrient content in compost. Apart from that, the production of liquid organic fertilizer with the main ingredient of agricultural waste significantly affects the macronutrient content of liquid organic fertilizer produced.

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“…[24][25][26] The CNNs models have been widely used in agriculture, medical care, education, energy, industrial inspection and other fields. Xue et al [27] have proposed a fast and easy method for predicting agricultural waste compost maturity by analyzing images of different composting stages, and it achieves accuracy of 99.7%, 99.4%, 99.7% and 99.5% on the 4 test sets, respectively. Park et al [28] have proposed a deep learning-based player evaluation model to analyze the positive or negative effect for baseball league.…”

Section: Introductionmentioning

confidence: 99%

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

Tan

Huang

et al. 2020

PLoS ONE

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Zanthoxyli Pericarpium (ZP) are the dried ripe peel of Zanthoxylum schinifolium Sieb. et Zucc (ZC) or Zanthoxylum bungeanum Maxim (ZB). It has wide range of uses both medicine and food, and favorable market value. The diverse specifications of components of ZP is exceptional, and the common aims of adulteration for economic profit is conducted. In this work, a novel method for the identification different species of ZP is proposed using convolutional neural networks (CNNs). The data used for the experiment is 5 classes obtained from camera and mobile phones. Firstly, the data considering 2 categories are trained to detect the labels by YOLO. Then, the multiple deep learning including VGG, ResNet, Inception v4, and DenseNet are introduced to identify the different species of ZP (HZB, DZB, OZB, ZA and JZC). In order to assess the performance of CNNs, compared with two traditional identification models including Support Vector Machines (SVM) and Back Propagation (BP). The experimental results demonstrate that the CNN model have a better performance to identify different species of ZP and the highest identification accuracy is 99.35%. The present study is proved to be a useful strategy for the discrimination of different traditional Chinese medicines (TCMs).

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A fast and easy method for predicting agricultural waste compost maturity by image-based deep learning

Cited by 47 publications

References 19 publications

Machine Learning‐Enabled Smart Sensor Systems

Machine Learning‐Enabled Smart Sensor Systems

Characteristics of the Macronutrient Content of Compost and Liquid Organic Fertilizer from Agricultural Wastes

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

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