Banana Plant Nutrient Deficiencies Identification using Deep Learning

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dc.contributor.authorHan, Kadipa Aung Myo
dc.contributor.authorManeerat, Noppadol
dc.contributor.authorSepsirisuk, Kasemsuk
dc.contributor.authorHamamoto, Kazuhiko
dc.date.accessioned2026-08-06T10:39:41Z
dc.date.available2026-08-06T10:39:41Z
dc.date.issued2023-01-01
dc.description.abstractThis paper presents nutrient deficiency multi-class classification in banana plant data sets using a deep convolutional neural network. In this paper, healthy and eight nutrient deficiency classes were studied. The performance was evaluated in different situations of two public data sets. The proposed method can provide sensitivity and specificity in Raw Images, Raw Images with combination, Augmented Images, and Augmented Images with the combination. Furthermore, nearly 88% of the F1-score was outperformed.
dc.identifier.citation2023 9th International Conference on Engineering Applied Sciences and Technology Iceast 2023 Proceeding, 5-9, 2023
dc.identifier.doi10.1109/ICEAST58324.2023.10157689
dc.identifier.other2-s2.0-85165691175
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13941
dc.source2023 9th International Conference on Engineering Applied Sciences and Technology Iceast 2023 Proceeding
dc.subjectbanana
dc.subjectconvolutional neural network
dc.subjectdeep learning
dc.subjectplant nutrient deficiencies
dc.titleBanana Plant Nutrient Deficiencies Identification using Deep Learning
dc.typeConference Paper

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