Deep Convolutional Neural Network Architectures for Tonal Frequency Identification in a Lofargram

2020 ◽  
Author(s):  
Jihun Park ◽  
Dae-Jin Jung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Network Architectures ◽  
Tonal Frequency ◽  
Frequency Identification ◽  
Neural Network Architectures

scholarly journals Classification of plant seedlings using deep convolutional neural network architectures

2022 ◽  
Vol 2161 (1) ◽  
pp. 012006
Author(s):  
Namratha Makanapura ◽  
C Sujatha ◽  
Prakash R Patil ◽  
Padmashree Desai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Weed Management ◽  
Early Stage ◽  
Vital Role ◽  
Deep Convolutional Neural Network ◽  
Network Architectures ◽  
Weed Species ◽  
Neural Network Architectures ◽  
Plant Seedlings

Abstract Weed management has a vital role in applications of agriculture domain. One of the key tasks is to identify the weeds after few days of plant germination which helps the farmers to perform early-stage weed management to reduce the contrary impacts on crop growth. Thus, we aim to classify the seedlings of crop and weed species. In this work, we propose a plant seedlings classification using the benchmark plant seedlings dataset. The dataset contains the images of 12 different species where three belongs to plant species and the other nine belongs to weed species. We implement the classification framework using three different deep convolutional neural network architectures, namely ResNet50V2, MobileNetV2 and EfficientNetB0. We train the models using transfer learning and compare the performance of each model on a test dataset of 833 images. We compare the three models and demonstrate that the EfficientNetB0 performs better with an average F1-Score of 96.26% and an accuracy of 96.52%.

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