Development of Detection System for Cotton Plastic Covering Using Ultrasonic

Zhang, Qi; Liang, Hua; Yao, Juan

doi:10.4028/www.scientific.net/amm.411-414.1439

Cited by 5 publications

(4 citation statements)

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“…Finally, across different countries that cultivate cotton, there have been studies aimed at detecting diverse types of plastic trash in cotton fields using remote sensing and image processing techniques [41,[81][82][83][84]. These techniques, which majorly use images taken from unmanned aerial vehicles (UAVs) with a single or multiple multispectral camera(s) on board, have shown good prospects for detecting plastic in cotton fields at different stages of crop physiological maturity and have thus been proposed for in-field management/mitigation of plastic contamination of seed cotton to create maps of plastic locations within cotton fields that can aid retrieval before harvesting [83]. However, most of the techniques have the disadvantage of being largely dependent on finding the right atmospheric conditions to capture the aerial image dataset.…”

Section: Plastic Mitigation Efforts: Pre-planting Pre-harvesting and ...mentioning

confidence: 99%

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

Adeleke¹

2023

Preprint

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Plastic contamination is a burning issue costing the global cotton and textile industries billions of dollars annually. Any time plastics from different sources end up in a cotton lint bale, the value to the textile mills plummets significantly. Various industry players have therefore made a concerted effort to find lasting solutions to the menace posed by plastic to cotton profitability and sustainability. Nevertheless, until now, there have been no up-to-date comprehensive documents detailing the numerous and ever-growing efforts committed to solving this challenge. Therefore, this article provides a detailed yet compact review of this highly dynamic subject matter. First, it puts into perspective plastic contamination in the cotton and textile industries. Then, the cotton value chain is subdivided into phases from pre-cultivation to textile mills. The root causes of plastic contamination are discussed in each stage, followed by discussions of some already developed and emerging solutions in response to the challenge by the affected industries and researchers. Concluding from the author’s perspective, the paper makes projections for the future directions of plastic mitigation efforts within the cotton and textile industries. This article also infers from the reviewed literature that research on finding alternative materials to plastic as module wrap, the development of new, effective, and all-condition plastic sensing techniques for ginning and spinning equipment, and standardized protocols for UAV in-field surveys of plastic trash are some of the areas that will be beneficial to finding a permanent solution to the challenge.

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Section: Plastic Mitigation Efforts: Pre-planting Pre-harvesting and ...mentioning

confidence: 99%

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

Adeleke¹

2023

Preprint

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“…Across different countries where cotton is cultivated, there have been some research studies aimed at detecting different types of plastic trash in cotton fields using remote sensing and image processing techniques [30,[71][72][73][74]. These techniques which mostly rely on imageries taken aboard unmanned aerial vehicles (UAVs) with one or multiple multispectral camera(s) on board have shown good prospects for the detection of plastic in cotton fields at different stages of the crop physiological maturity and thus, have been proposed for in-field managing/mitigation of plastic contamination of seed cotton to create maps of plastic locations within cotton fields that can aid retrieval before harvesting [74].…”

Section: Plastic Mitigation Efforts: Pre-planting Pre-harvesting and ...mentioning

confidence: 99%

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

Adeleke¹

2022

Preprint

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Plastic contamination is a topical issue in the cotton and textile industries. These plastic contaminants come from diverse sources, including agricultural mulch films, improperly disposed plastic trash near cotton fields, and importantly, the plastic wraps used to cover cylindrical modules built by John Deere’s onboard module builder harvesters. When these different plastic materials end up in the cotton lint bales, the value of the bale to the textile mills plummets significantly. To that end, there has been a concerted effort by the two major industries by this issue to find lasting solutions to the menace posed to their profitability by plastic. In this review article, the subject of plastic contamination within these industries is first put into perspective. Thereafter, the cotton value chain is subdivided into different phases from pre-cultivation to textile mills. The root causes of plastic contamination in each of the phases are then analyzed, followed by discussions of the select solutions that have been developed or are being developed in response to the challenge by the industry and researchers. Finally, from the author’s perspective, brief projections for the future direction of plastic mitigation efforts within the cotton and textile industries are presented. This review is envisaged to serve as a useful reference for new hands starting their research career in cotton- and textile-related industries, as well as practicing professionals needing to keep abreast of developments in the industry.

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“…Ultrasonic sensors identify plastic film according to different densities between plastic film and cotton. However, the relatively low speed for ultrasonic transmission has resulted in a slow identification process [4]. With the development of computer technologies, computer vision techniques with the advantages of low costs, fast speed and consistency have been widely used in foreign matter detection [5]- [11].…”

Section: Introductionmentioning

confidence: 99%

Online Sorting of the Film on Cotton Based on Deep Learning and Hyperspectral Imaging

Zhang

et al. 2020

IEEE Access

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Mulch film is usually mixed in with cotton during machine-harvesting and processing, which reduces the cotton quality. This paper presents a novel sorting algorithm for the online detection of film on cotton using hyperspectral imaging with a spectral region of 1000-2500 nm. The sorting algorithm consists of a group of stacked autoencoders, two optimization modules and an extreme learning machine (ELM) classifier. The variable-weighted stacked autoencoders (VW-SAE) are constructed to extract the features from hyperspectral images, and an artificial neural network (ANN), which is one optimization module, is applied to optimize the parameters of the VW-SAE. Then, the extracted features are input in the ELM to classify four types of objects: background, film on background, cotton and film on cotton. The ELM is optimized by a new optimizer (grey wolf optimizer), which can adjust the hidden nodes and parameters of the ELM simultaneously. A group of experiments was carried out to evaluate the performance of the proposed sorting algorithm using cotton that was provided by a Xinjiang municipality cotton ginning company. The experimental results show that the VW-SAE can improve the classification accuracies by approximately 15 %. The overall recognition rate of the proposed algorithm is over 95 %, and its recognition time is comparable to some state-of-the-art methods. INDEX TERMS Cotton, sorting system, plastic film, deep learning, hyperspectral imaging, grey wolf optimizer, variable-wise weighted stacked autoencoder.

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Development of Detection System for Cotton Plastic Covering Using Ultrasonic

Cited by 5 publications

References 1 publication

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

A Review of Plastic Contamination Challenges and Mitigation Efforts in Cotton and Textile Milling Industries

Online Sorting of the Film on Cotton Based on Deep Learning and Hyperspectral Imaging

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