Plants recognition using embedded Convolutional Neural Networks on Mobile devices

Pechebovicz, Denise; Premebida, Sthefanie; Soares, Vinicios R.; Camargo, Thiago; Bittencourt, Jakson L.; Baroncini, Virginia; Martins, Marcella Scoczynski Ribeiro

doi:10.1109/icit45562.2020.9067289

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Research on the identification of phytomedicine plant leaves has been carried out by several previous studies, for example Naresh and Nagendraswamy in 2015 [22], Mukherjee and his team in 2016 [23], Venkataraman & Mangayarkarasi in 2017 [24], Gao & Lin in 2018 [25], Sivaranjani et al in 2019 [26], Pechebovicz et al in 2020 [27], Bhuiyan et al in 2021 [28].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Medicinal Plant Leaves Recognition: A Comparison with and without a Fine-Tuning Strategy

Ayumi¹,

Ermatita²,

Abdiansah³

et al. 2022

IJACSA

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Plant leaves are another common source of information for determining plant species. According to the dataset that has been collected, we propose transfer learning models VGG16, VGG19, and MobileNetV2 to examine the distinguishing features to identify medicinal plant leaves. We also improved algorithm using fine-tuning strategy and analyzed a comparison with and without a fine-tuning strategy to transfer learning models performance. Several protocols or steps were used to conduct this study, including data collection, data preparation, feature extraction, classification, and evaluation. The distribution of training and validation data is 80% for training data and 20% for validation data, with 1500 images of thirty species. The testing data consisted of a total of 43 images of 30 species. Each species class consists of 1-3 images. With a validation accuracy of 96.02 percent, MobileNetV2 with finetuning had the best validation accuracy. MobileNetV2 with finetuning also had the best testing accuracy of 81.82%.

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