Applications of computer vision techniques to cotton foreign matter inspection: A review

Zhang, Hang; Li, Bingbing

doi:10.1016/j.compag.2014.09.004

Cited by 44 publications

(24 citation statements)

References 44 publications

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“…Moreover, the sensors that have already been explored can be further explored by replicating previous techniques to smartphones. Computer vision techniques have been studied in the context of agriculture extensively, many of which were proposed as offline computation, for example, weed identification [49] and cotton foreign matter inspection [50]. The current state of server computational power can allow some of these algorithms to be deployed where images are taken using smartphone cameras, sent to server for processing, and the results are returned to senders on their mobile phones.…”

Section: Extension Of Sensor Usage From Existingmentioning

confidence: 99%

Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research

2015

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Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device is highly accessible, and their computing power allows a variety of practical applications to be created. Moreover, smartphones are nowadays equipped with various types of physical sensors which make them a promising tool to assist diverse farming tasks. This paper systematically reviews smartphone applications mentioned in research literature that utilize smartphone built-in sensors to provide agricultural solutions. The initial 1,500 articles identified through database search were screened based on exclusion criteria and then reviewed thoroughly in full text, resulting in 22 articles included in this review. The applications are categorized according to their agricultural functions. Those articles reviewed describe 12 farming applications, 6 farm management applications, 3 information system applications, and 4 extension service applications. GPS and cameras are the most popular sensors used in the reviewed papers. This shows an opportunity for future applications to utilize other sensors such as accelerometer to provide advanced agricultural solutions.

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Section: Extension Of Sensor Usage From Existingmentioning

confidence: 99%

Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research

2015

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“…Spectral model identification makes reference to the set of categorization processes that employs the pixel-per-pixel spectral data like the foundation for computerized land cover categorization [33]. Spatial form recognition holds the categorization of illustration pixels on the basis of the spatial connection among pixels neighboring them [34]. These sorts of groups try to repeat the type of spatial fusion done by means of special analysts through ocular analysis operations found on picture quality and texture, pixel nearness, attribute dimension, contour, and duplication, in addition to environment.…”

Section: Imagery Treatment and Prediction In Mappingmentioning

confidence: 99%

Remote Sensing: An Advanced Technique for Crop Condition Assessment

Ennouri

Kallel

2019

Mathematical Problems in Engineering

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Actually, cultivators are increasingly arranging innovative high technical and scientific estimations in the aim to enhance agricultural sustainability, effectiveness, and/or plant health. Innovative farming technologies incorporate biology with smart agriculture: computers and devices exchange with one another autonomously in a structured farm management system. Throughout this structure, smart agriculture can be accomplished; cultivators decrease plantation inputs (pesticides and fertilizers) and increase yields via integrated pest management and/or biological control. The emerging concept of remote sensing may provide a framework to systematically consider these issues of smart farming technology and to embed high-tech agriculture better. The impact(s) may be beneficial depending on how tools, such as data mining, and imagery technologies, such as picture treatment and analysis, are applied. Remote sensing technology is discussed in this review and demonstrates its possibility to create novel opportunities for scientists (and agronomists) to explore aspects of biological phenomena that cannot be accessed through usual mechanisms or processes.

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“…xx (14) III. CLASSIFICATION ALGORITHM The proposed algorithm is described as follows: 1) Obtain an RGB picture of the nut in the conveyor belt (Fig.…”

Section: E Euclidian Distancementioning

confidence: 99%

“…HE computer vision techniques have been developed since 1960's, and continued growing as in theory and applications [1], [2]. Nowadays, these techniques are used in a wide range of applications, such as medical imaging [3]- [5], industry automation [6], [7], monitoring [8], food quality [9]- [11], quality assessment [12], [13], among others [14], [15]. The product quality depends on how the industry processes are performed.…”

Section: Introductionmentioning

confidence: 99%

Naïve Screw Nut Classifier Based on Hu's Moment Invariants and Minimum Distance

et al. 2018

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In this paper, an algorithm for classification of screw nuts by means of digital image processing is presented. This work is part of a project where a production line was built, and is focused on the quality assessment section. The algorithm presented classifies among good and poor quality screw nuts passing by a conveyor belt, by computing Hu's moment invariants of its picture. Those moment invariants are the input of a minimum distance classifier, obtaining very competitive results compared with some other classification algorithms of the WEKA plattform.

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Applications of computer vision techniques to cotton foreign matter inspection: A review

Cited by 44 publications

References 44 publications

Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research

Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research

Remote Sensing: An Advanced Technique for Crop Condition Assessment

Naïve Screw Nut Classifier Based on Hu's Moment Invariants and Minimum Distance

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