Predicting maturity of coconut fruit from acoustic signal with applications of deep learning

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dc.contributor.authorSattar, Farook
dc.date.accessioned2024-10-10T17:23:11Z
dc.date.available2024-10-10T17:23:11Z
dc.date.issued2024
dc.description.abstractThis paper aims to develop an effective AI-driven method to predict the maturity level of coconut (Cocos nucifera) fruits using acoustic signals. The proposed sound-based autonomous approach exploits various deep learning models, including customized CNN pretrained networks, i.e., the ResNet50, InceptionV3, and MobileNetV2, models for maturity level classification of the coconuts. The proposed study also demonstrates the effectiveness of various deep learning models to automatically predict the maturity of coconuts into three classes, i.e., premature, mature, and overmature coconuts, for inspecting the coconut fruits by using a small amount of input acoustic data. We use an open-access dataset containing a total of 122 raw acoustic signals, which is the result of knocking 122 coconut samples. The results achieved by the proposed method for coconut maturity prediction are found to be promising, which enables producers to accurately determine the yield and product quality.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.identifier.citationSattar, F. (2024). Predicting maturity of coconut fruit from acoustic signal with applications of deep learning. Biology and Life Sciences Forum, 30(1), Article 1. https://doi.org/10.3390/IOCAG2023-16880
dc.identifier.urihttps://doi.org/10.3390/IOCAG2023-16880
dc.identifier.urihttps://hdl.handle.net/1828/20552
dc.language.isoen
dc.publisherBiology and Life Sciences Forum
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectacoustic signals
dc.subjectartificial intelligence
dc.subjectcoconut fruit
dc.subjectdeep learning
dc.subjectfruit quality
dc.subjectmaturity levels
dc.subjectprediction
dc.titlePredicting maturity of coconut fruit from acoustic signal with applications of deep learning
dc.typeArticle

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