Non-destructive detection of codling moth infestation in apples using acoustic impulse response signals

Khaled, Alfadhl Yahya; Ekramirad, Nader; Parrish, Chadwick A.; Eberhart, Paul; Doyle, L. Austin; Donohue, Kevin D.; Villanueva, Raul T.; Adedeji, Akinbode A.

doi:10.1016/j.biosystemseng.2022.10.001

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…ML models with a smartphone camera can also be implemented as a low-cost technique to detect the quality and maturity of crops during storage and distribution (Cavallo et al ., 2019; Cho et al ., 2020; de Souza et al ., 2021; Garre et al ., 2020). ML is therefore seen as a viable option for the development and implementation of non-destructive evaluation as well as sorting and grading processes for varied cultivars (Dhakshina Kumar et al ., 2020; Knott et al ., 2023; Khaled et al ., 2022).…”

Section: Results and Analysismentioning

confidence: 99%

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Yuvaraj

Basu

Abdulrahman

et al. 2023

IMDS

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PurposeInformation and communication technology (ICT) implementation has demonstrated usefulness in supply chain coordination and efficiency optimization in various industries and sectors. This study investigates the extent of ICT deployment in fruits and vegetable supply chains (FVSC) from “farm-to-fork” to ensure food security.Design/methodology/approachThis paper employs a systematic literature review (SLR) methodology and identified a total of 99 journal articles ranging from 2001 to April 2023 for analysis. The reviewed articles have been classified based on the framework proposed from the perspective of food security. Bibliometric and content analysis is carried out with the final list of articles to extract useful insights.FindingsThe findings reveal that ICT implementation in FVSC is a relatively new research area; researchers have started investigating several aspects of ICT in FVSC through varied research methodologies. Experimental research aimed at addressing food safety and condition monitoring of fruits and vegetables (FV) has started to gain traction while theory building is yet to gain traction in the literature reviewed. Findings indicate further research is required on technologies like blockchain (BCT), artificial intelligence (AI) and machine learning (ML), especially on key objectives such as food security, and the triple-bottom-line approach of sustainability. It also indicates that implementing relevant ICTs in FVSC can help delay, if not avert, the food crisis predicted by Malthusian theory.Research limitations/implicationsThis study used only well-established databases to ensure quality of the studies examined. There is a possibility of missing out on articles from other sources not considered. As a result, future SLR studies may employ additional databases, such as Springer Link, Taylor and Francis, Emerald Insight and Google Scholar. Other methodologies such as expert interviews and extra empirical methodologies may also be employed to give a more balanced picture and insights into ICTs implementation in FVSC.Practical implicationsThis study offers a summative detail of the status of ICT implementation in FVSC and can serve as a reference guide for stakeholders in developing strategies for efficient FVSC management. This research work highlights the impact of ICT implementation in FVSC on the four pillars of food security which include improved availability, accessibility, utilization and stability.Originality/valueThis study focuses on ICT implementation for food security in FVSC. The SLR highlights the gaps and proffers potential solutions that enhance global efforts on food security through ICT-enabled reduction in food waste and food loss in FVSC.

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Section: Results and Analysismentioning

confidence: 99%

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Yuvaraj

Basu

Abdulrahman

et al. 2023

IMDS

View full text Add to dashboard Cite

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“…Although these two types of studies differ in the mechanism of vocalization, the audio is recognized approximately in the same way. Generally, it is first to extract the audio signal features and then to develop audio recognition models by using mathematical methods (Gao et al, 2010) or machine learning (e.g., backpropagation neural network [BPNN]) (Khaled et al, 2022; Zhang et al, 2019). If canopy excited audios by the wind from air‐assisted sprayers can be gathered and then processed under these two steps, leaf densities can be determined.…”

Section: Introductionmentioning

confidence: 99%

Leaf‐density estimation for fruit‐tree canopy based on wind‐excited audio

Li,

Jiang,

Yang

et al. 2024

Journal of Field Robotics

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It is important to obtain real‐time leaf density of fruit‐tree canopies for the precision spray control of plant‐protection robots. However, conventional detection techniques for the characteristics of fruit‐tree canopies cannot acquire the canopy internal information, which may provide an unsatisfactory accuracy of detection of leaf densities. This paper proposes a method for estimating canopy leaf density of fruit trees based on wind‐excited audio. A wind‐exciting implement was used to force fruit‐tree canopy leaves vibrating to produce audio. Then, some correlation analysis methods were used to extract key characteristic parameters of wind‐excited audio that were significantly correlated with leaf density. Finally, based on the data set of wind‐excited audio, a few machine‐learning methods were used to develop leaf‐density estimation models. Test results showed that: (1) there were five key feature parameters of wind‐excited audio that were significantly correlated with leaf density: the short‐time energy, spectral centroid, the frequency average energy, the peak frequency, and the standard deviation of frequency. (2) the estimation model of leaf density developed based on backpropagation neural network for fruit‐tree canopy showed the optimal estimation results, which can achieve the estimation of leaf density of fruit‐tree canopies accurately. The overall correlation coefficient (R) of the estimation model was more than 0.84, the root‐mean‐square error was less than 0.73 m2 m−3, and the mean absolute error was less than 0.53 m2 m−3. This study is expected to provide a technical solution for the leaf‐density detection of fruit‐tree canopies of plant‐protection robots.

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“…Acoustic sensing technology is a highly effective technique in the field of remote sensing, utilizing the physical properties of sound [1][2][3][4][5][6][7][8][9][10][11]. In the last decade, considerable progress has been made in improving the signal-to-noise ratio (SNR) in electroacoustic sensor device applications.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Gradient-Coiling Metamaterial for Enhanced Acoustic Signal Sensing

Hao,

Zhao,

Han

2023

Crystals

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Acoustic sensing systems play a critical role in identifying and determining weak sound sources in various fields. In many fault warning and environmental monitoring processes, sound-based sensing techniques are highly valued for their information-rich and non-contact advantages. However, noise signals from the environment reduce the signal-to-noise ratio (SNR) of conventional acoustic sensing systems. Therefore, we proposed novel nonlinear gradient-coiling metamaterials (NGCMs) to sense weak effective signals from complex environments using the strong wave compression effect coupled with the equivalent medium mechanism. Theoretical derivations and finite element simulations of NGCMs were executed to verify the properties of the designed metamaterials. Compared with nonlinear gradient acoustic metamaterials (Nonlinear-GAMs) without coiling structures, NGCMs exhibit far superior performance in terms of acoustic enhancement, and the structures capture lower frequencies and possess a wider angle acoustic response. Additionally, experiments were constructed and conducted using set Gaussian pulse and harmonic acoustic signals as emission sources to simulate real application scenarios. It is unanimously shown that NGCMs have unique advantages and broad application prospects in the application of weak acoustic signal sensing, enhancement and localization.

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Non-destructive detection of codling moth infestation in apples using acoustic impulse response signals

Cited by 10 publications

References 29 publications

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Leaf‐density estimation for fruit‐tree canopy based on wind‐excited audio

A Nonlinear Gradient-Coiling Metamaterial for Enhanced Acoustic Signal Sensing

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