Using machine learning for image-based analysis of sweetpotato root sensory attributes

Nakatumba-Nabende, Joyce; Babirye, Claire; Tusubira, Jeremy Francis; Mutegeki, Henry; Nabiryo, Ann Lisa; Murindanyi, Sudi; Katumba, Andrew; Nantongo, Judith; Sserunkuma, Edwin; Nakitto, Mariam; Ssali, Reuben; Makunde, Godwill; Moyo, Mukani; Campos, Hugo

doi:10.1016/j.atech.2023.100291

Cited by 7 publications

(1 citation statement)

References 61 publications

(71 reference statements)

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“…In recent years, with the rapid development of information technology and the popularization and application of big data, machine learning, as a powerful tool for data analysis and recognition, has achieved remarkable results in various fields [ 11 , 12 , 13 , 14 , 15 ]. In the field of TBMs, machine learning has revolutionized traditional engineering and construction methods [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Zhou,

Liu,

et al. 2024

Sensors

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In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV−SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring process, which diminishes the boring efficiency and is challenging to detect during construction. This paper uses SolidWorks to create a complete three−dimensional model of the TBM hydraulic thrust system and tool system. Then, dynamic simulations are performed with Adams. This helps us understand how the load on the propulsion hydraulic cylinder changes as the TBM tunneling tool wears to different degrees during construction. The hydraulic propulsion system was modeled and simulated using AMESIM software. Utilizing the load on the hydraulic propulsion cylinder as an input signal, pressure signals from the two chambers of the hydraulic cylinder and the system’s flow signal were acquired. This enabled an in−depth exploration of the correlation between these acquired signals and the extent of the tooling system failure. Following this analysis, a collection of normal sample data and sample data representing different degrees of disk cutter abrasions was amassed for further study. Next, an SSFL network model for locating the failure area of the cutter was established. Fault sample data were used as the input, and the accuracy of the fault diagnosis model was tested. The test results show that the performance of the SSFL network model is better than that of the SAE−SVM and SVDD network models. The SSFL model achieves 90% accuracy in determining the failure area of the cutter head. The model effectively identifies the failure regions, enabling timely tool replacement to avoid decreased boring efficiency under wear conditions. The experimental findings validate the feasibility of this approach.

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Section: Introductionmentioning

confidence: 99%

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Zhou,

Liu,

et al. 2024

Sensors

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Developing and Deploying End‐to‐End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts

Bainomugisha,

Nakatumba‐Nabende

2024

Applied AI Letters

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Artificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real‐world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.

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NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from ‘Farm’ to ‘Table’: Progresses, challenges, trends, and prospects

Wang,

Xing,

et al. 2024

Food Chemistry: X

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Using machine learning for image-based analysis of sweetpotato root sensory attributes

Cited by 7 publications

References 61 publications

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System

Developing and Deploying End‐to‐End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts

NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from ‘Farm’ to ‘Table’: Progresses, challenges, trends, and prospects

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