Assessment of Orchid Surfaces Using Top-Down Contact Angle Mapping

Janeczko, Cesar; Martelli, Cícero; Canning, John; Dutra, Guilherme

doi:10.1109/access.2019.2902730

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…If it is hydrophilic h will be less than r cap . However, if it is larger, then the volume is not correct and instead, the following volume is used to obtained h [24]: …”

Section: Methodsmentioning

confidence: 99%

Towards a bionic IoT: environmental monitoring using smartphone interrogated plant sensors

Canning

Guo

Chaczko

2022

Preprint

Self Cite

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The utilisation of plants directly as quantifiable natural sensors is proposed. A case study measuring surface wettability of Aucuba japonica, or Japanese Laurel, plants using a novel smartphone field interrogator is demonstrated. This plant has been naturalised globally from Asia. Top-down contact angle measurements map wettability on-site and characterise a range of properties impacting plant health, such as aging, solar and UV exposure, and pollution. Leaves at an early age or in the shadow of trees are found to be hydrophobic with contact angle q ~ 99°, while more mature leaves under sunlight are hydrophilic with q ~ 79°. Direct UVA irradiation at l = 365 nm is shown to accelerate aging, changing contact angle of one leaf from slightly hydrophobic at q ~ 91° to be hydrophilic with q ~ 87 ° after 30 min. Leaves growing beside a road with heavy traffic are observed to be substantially hydrophilic, as low as q ~ 47°, arising from increased wettability with particulate accumulation on the leaf surface. Away from the road, the contact angle increases as high as q ~ 96°. The results demonstrate that contact angle measurements using a portable diagnostic IoT edge device can be taken into the field for environmental detection, pollution assessment and more. Using an internet connected smartphone combined with a plant sensor allows multiple measurements at multiple locations together in real-time, potentially enabling tracking of parameter change anywhere where plants are present or introduced. This hybrid integration of widely distributed living organic systems with the internet marks the beginning of a new bionic internet-of-things ( b -IoT).

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“…If it is hydrophilic h will be less than r cap . However, if it is larger, then the volume is not correct and instead, the following volume is used to obtained h [24]: …”

Section: Methodsmentioning

confidence: 99%

Towards a bionic IoT: environmental monitoring using smartphone interrogated plant sensors

Canning

Guo

Chaczko

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, machine learning methods like the naive Bayes method have been employed for pest and disease diagnosis in black orchid plants, demonstrating their effectiveness in automated disease detection [22]. Furthermore, advancements in surface assessment technologies, such as top-down contact angle mapping, have been explored for evaluating orchid surfaces, providing insights into plant health and growth conditions [23]. In addition, innovative ranging technologies, including height-variable monocular vision, have been developed for smart agriculture applications, enabling precise monitoring and management of crop growth [24].…”

Section: History Of Researchmentioning

confidence: 99%

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

2024

tjes

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Target area detection in agriculture plays a crucial role in optimizing crop management and resource allocation. Traditional methods often lack the precision and efficiency required for modern farming practices. Integrating bio-inspired approaches with UAV-FSN systems offers a promising solution to this challenge. By harnessing principles from nature, such as swarm intelligence and reinforcement learning, it becomes possible to optimize the deployment and coordination of UAVs within FSN for clustered target area detection in agriculture. This research explores the integration of bio-inspired approaches with UAV-FSN systems for clustered target area detection in agriculture. The increasing demand for precision agriculture necessitates efficient methods for identifying target areas affected by various factors. In this study, we introduce a novel algorithm, the QL-ABC Algorithm, which combines the Artificial Bee Colony (ABC) Algorithm with Q-Learning to optimize the deployment and movement of unmanned aerial vehicles (UAVs) within flying sensor networks (FSN) for target area detection. The performance outcomes of the proposed QL-ABC Algorithm on comparison with the existing algorithms are analysed using extensive simulation analysis with the appropriate simulation metrics: packet delivery ratio, mean end-to-end delay, and energy consumption. Results validate the usefulness of the proposed QL-ABC algorithm in accurately detecting clustered target areas in agricultural landscapes, thus contributing to advancements in precision farming practices and sustainable agriculture. Keywords: Flying Sensor Networks, Artificial Bee Colony Algorithm, Clustered Target Area Detection, Q-Learning, Unmanned Aerial Vehicles.

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“…Top-down contact angle measurements were used to measure Cattleya warneri orchids and Dracaena draco at different ages and it was observed that the older leaves showed hydrophilicity while the younger leaves showed hydrophobicity (Janeczko et al, 2019;Jura-Morawiec and Marcinkiewicz, 2020). Unique leaf structures on the surface of young leaves during their developmental stages, such as various papillae, play an important role in the increase of wettability.…”

Section: Growth Agementioning

confidence: 99%

Factors influencing wettability and surface/interface mechanics of plant surfaces: a review

Tie,

Gao,

Huang

et al. 2023

Front. Mater.

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A wide variety of abundant plant leaves exist in nature, and the wettability of their surfaces is formed to adapt to diverse external environments. In this paper we will focus on the factors influencing the wettability of various plant leaves prevalent in nature. And we hope to investigate the interfacial problems of plants from a mechanical point of view. It is found that there are many factors affecting the surface wettability of leaves, such as chemical composition, surface microstructures, hierarchical structures, and growth age. Different influencing factors have different contributions to the change of surface wettability. The surface wax composition influences the surface wettability from a chemical point of view while the hierarchical structure consisting of nanostructures and micron structures also influences the wettability from a structural point of view. Also as the growth age of the plant increases, there is a combined effect on the chemical composition and microstructure of the leaves. Then we discuss the surface/interface mechanics of droplets on various plant leaves and analyze the wetting properties of droplets on different substrates. Finally, we hope that the surface/interface mechanics of plant leaves may be systematically utilized in the future for the preparation of multifunctional biomimetic materials, realizing the crossover of chemistry, biology, mechanics, and other materials science fields.

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Assessment of Orchid Surfaces Using Top-Down Contact Angle Mapping

Cited by 8 publications

References 30 publications

Towards a bionic IoT: environmental monitoring using smartphone interrogated plant sensors

Towards a bionic IoT: environmental monitoring using smartphone interrogated plant sensors

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

Factors influencing wettability and surface/interface mechanics of plant surfaces: a review

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