Development, evaluation, and optimization of an automated device for quality detection and separation of cowpea seeds

Audu, J.; Aremu, Abdulfatai

doi:10.1016/j.aiia.2021.10.003

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…These optical inspection techniques can be mainly used to measure the spectral information of agricultural products to obtain the diffuse reflectance (or transmittance) of the samples and then combine this spectral information with existing chemometrics algorithms to establish a prediction model for the quality of agricultural products. Although existing intelligent information processing techniques are more mature, such as deep learning and machine learning ( Audu and Aremu, 2021 ; Dhanya et al., 2022 ; Ryo, 2022 ), these methods have been widely developed and can further enhance the ability to detect the quality of agricultural products. Nevertheless, the spectral information which has already been obtained, can be only analyzed by these methods, and if the spectral information obtained is better, then the quality of agricultural products will be more accurately detected.…”

Section: Introductionmentioning

confidence: 99%

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

Xia,

Liu,

Meng

et al. 2024

Front. Plant Sci.

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Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper.

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Section: Introductionmentioning

confidence: 99%

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

Xia,

Liu,

Meng

et al. 2024

Front. Plant Sci.

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“…One of the fastest ways to detect quality in an industry setting is by employing the knowledge of artificial intelligence through machine learning [ 6 ]. According to Ref.…”

Section: Introductionmentioning

confidence: 99%

Development of two smart acoustic yam quality detection devices using a machine learning approach

Audu

Dinrifo

Adegbenjo

et al. 2023

Heliyon

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“…Artificial Intelligence is intelligence exhibit by a machine. In a more explicit sense it is the development of computer systems that has the ability to perform tasks that usually require human intelligence (Saleh, 2019;Bezboruah andBora, 2020, Audu andAremu, 2021). Automation is a technical term usually associated with artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Development of a Smart Grain Storage Silo Using the Internet of Things (IoT) Technology

E.S.A.¹,

Adediran²,

Adekanye³

et al. 2022

IJAIML

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Food security is the aspiration of every nation. To achieve this, particularly in Developing Countries, there is a need to reduce wastage by storing staple foods grains beyond their production seasons. Longer storage requires human presence, monitoring and control of the storage environment which may be laborious, demanding and sometimes outrightly unsafe. Therefore, the needs to employ automation and artificial intelligence become necessary to control this storage environment. This study developed an automated, intelligent silo bin that controls the storage environment of the system for the small-scale rural farmers, of which over 70% of their population still depend on agriculture, using Internet of Things (IoT). The developed system consists of three units interfaced together. These units are the pro-type 2-ton (2,000 kg) silo structure, the embedded system (made up of the microcontroller, sensors and relays). The system is integrated to an IoT system (made up of mobile application (BLYNK), Wi-Fi module and ultrasonic atomizer) and the air blowing system (consisting of blower fan and heater). The developed smart system was tested and the test run results showed that it successfully monitors and controls storage air temperature, humidity, air pressure, grain moisture, insect infestation and CO 2 levels, the key parameters for long term storability of grains. The coding process could be set to suit different grains and storage conditions required for their effective storage. Although the silo bin structure used for testing was for a particular proto-type, it can be geometrically scaled for many silo structures.

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Development, evaluation, and optimization of an automated device for quality detection and separation of cowpea seeds

Cited by 4 publications

References 7 publications

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

Development of two smart acoustic yam quality detection devices using a machine learning approach

Development of a Smart Grain Storage Silo Using the Internet of Things (IoT) Technology

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