Green Technology

Sadiku, Matthew N. O.; Musa, Sarhan M.; Omotoso, Adebowale E. Shadare | Adedamola A.

doi:10.31142/ijtsrd20199

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Green Technology benefits in improving the quality by providing the sustainable atmosphere and also recycles waste material, purifies water and air, balances energy and revives ecosystems. Though green technology offers various benefits, it is indeed a challenging task as they are more costly in comparison to the recognized baseline technologies as it accounts for the environmental expenses (Sadiku et al., 2018). Green technology operates in various fields, mainly, bio‐fuel, eco‐forestry, renewable energy and solid waste management.…”

Section: Green Technology For Removal Of Mpsmentioning

confidence: 99%

Review of green technologies for the removal of microplastics from diverse environmental sources

Namasivayam,

Avinash

2023

Environmental Quality Mgmt

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Microplastics are the basic particles (1 µm–1 mm), derived from degradation of polymers with maximum molecular weight via various sources. They occur in a variety of shapes and sizes, in the form of domains, particles, and fibers. Release of Microplastics in the ecosystem, due to deterioration of macroplastics are now being considered as major toxic pollutants. Accumulation of microplastics in the diverse ecosystems is known to cause severe health and ecological effects. Since it is mandatory to take steps towards the removal of microplastics through effective and safe technology, with this objective, the present study is undertaken to reveal various green technology principles for the management of microplastics contamination associated with diverse environmental sources. This strategy involves physical methods, chemical methods, biological methods that under the conventional measures and nanotechnology principles as modern remediation. Physical methods involve filtration, membrane‐technology and adsorption. Chemical remediation involves hydrolysis, alcoholysis, acidolysis, glycolysis, aminolysis, photocatalytic degradation and electrochemical oxidation resulting in decomposition of microplastics into simpler, non‐toxic end products. Biological methods involve biosorption, microbial degradation, microbial ingestion and phytoremediation, where plants or plant biomass can be used for removal of microplastics via phytoaccumulation, phytostabilization, phytovolatilization and phytofiltration. Nano technology remediation utilizes nanoscale materials as adsorbents. Due to its unique nanosize, high surface area and energy, nanomaterials are considered effective adsorbents of microplastics. Microplastics can also be detected and identified using machine learning approaches. The present study implies that, the green technology principles can be effectively utilized for the removal of microplastics associated with diverse ecosystem.

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Section: Green Technology For Removal Of Mpsmentioning

confidence: 99%

Review of green technologies for the removal of microplastics from diverse environmental sources

Namasivayam,

Avinash

2023

Environmental Quality Mgmt

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“…The implementation of "green agriculture" and "organic agriculture" [1] can be supported by artificial intelligence algorithms. For instance, reducing the consumption of freshwater used in agriculture can be achieved with the help of systems that signal the need to irrigate, while productivity increases can be achieved with models that recommend the date of cultivation, date of harvesting, or even the type of crop to grow.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Classification System for Foliar Diseases in Sunflower

Dawod

Dobre

2022

Sustainability

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Obtaining a high accuracy in the classification of plant diseases using digital methods is limited by the diversity of conditions in nature. Previous studies have shown that classification of diseases made with images of lesions caused by diseases is more accurate than a classification made with unprocessed images. This article presents the results obtained when classifying foliar diseases in sunflower using a system composed of a model that automatically segments the leaf lesions, followed by a classification system. The segmentation of the lesions was performed using both Faster R-CNN and Mask R-CNN. For the classification of diseases based on lesions, the residual neural networks ResNet50 and ResNet152 were used. The results show that automatic segmentation of the lesions can be successfully achieved in the case of diseases such as Alternaria and rust, in which the lesions are well-outlined. In more than 90% of the images, at least one affected area has been segmented. Segmentation is more difficult to achieve in the cases of diseases such as powdery mildew, in which the entire leaf acquires a whitish color. Diseased areas could not be segmented in 30% of the images. This study concludes that the use of a system composed of a network that segments lesions, followed by a network that classifies diseases, allows us to both more accurately classify diseases and identify those images for which a precise classification cannot be made.

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Introduction to Green Technologies

Sadiku¹

2020

Emerging Green Technologies

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Green Technology

Cited by 4 publications

References 2 publications

Review of green technologies for the removal of microplastics from diverse environmental sources

Review of green technologies for the removal of microplastics from diverse environmental sources

Automatic Segmentation and Classification System for Foliar Diseases in Sunflower

Introduction to Green Technologies

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