Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection
نویسندگان
چکیده
In previous studies, researchers have determined the classification of fruit ripeness using feature descriptor color features (RGB, GSL, HSV, and L * a b *). However, performance from experimental results obtained still yields that are less than maximum, viz maximal accuracy is only 76%. Today, transfer learning techniques been applied successfully in many real-world applications. For this reason, propose VGG16 model. The proposed architecture uses without top layer. layer replaced by adding Multilayer Perceptron (MLP) block. MLP block contains Flatten layer, Dense Regularizes. output softmax activation function. There three Regularizes considered namely Dropout, Batch Normalization, kernels. selected intended to reduce overfitting. conducted on dataset was created researchers. Based found has better performance. Determination type very influential system best Dropout 0.5 with increased reaching 18.42%. Normalization kernels amount 10.52% 2.63%, respectively. This study shows deep always gets machine traditional extraction determines detection. gives also declaring technique overfitting learning.
منابع مشابه
Selective detection of ethylene gas using carbon nanotube-based devices: utility in determination of fruit ripeness.
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ژورنال
عنوان ژورنال: International journal of intelligent systems and applications
سال: 2021
ISSN: ['2074-904X', '2074-9058']
DOI: https://doi.org/10.5815/ijisa.2021.02.04