Classification of Cicer arietinum varieties using MobileNetV2 and LSTM

Gölcük, Adem; Yaşar, Ali; Saritaş, Mücahid Mustafa; Erharman, Ahmet

doi:10.1007/s00217-023-04217-w

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…In this way, it can perform the training and estimation by taking the input as a string, not with the current input to the network. LSTM networks differ from CNNs in that LSTM cells contain 3 gates (Golcuk et al., 2023 ). The LSTM cell contains the entrance gate, exit gate, and forget gate.…”

Section: Methodsmentioning

confidence: 99%

Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models

Isik,

Tasdemir,

Taspinar

et al. 2023

Food Science & Nutrition

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The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short‐Term Memory) and BiLSTM (Bi‐directional Long Short‐Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.

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Section: Methodsmentioning

confidence: 99%