Smart Seed Classification System based on MobileNetV2 Architecture
The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniqu...
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| Main Authors: | , , , , |
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| Format: | Proceeding Paper |
| Language: | en en |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/101722/6/101722_Smart_Seed_Classification_System_based_on_MobileNetV2_Architecture.pdf http://irep.iium.edu.my/101722/1/Screen%20Shot%202022-12-07%20at%208.39.52%20AM.png http://irep.iium.edu.my/101722/ https://ieeexplore.ieee.org/document/9711662 |
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| Summary: | The agricultural transformation in the last decade
using artificial intelligence has led to significant gains in
productivity and profitability. The traditional machine learning
approaches present inherent limitations in extracting features
and information from image data. Deep learning techniques,
particularly CNN’s, help to overcome these limitations due to
their multi-level architecture. Various deep learning
applications in agriculture include crop disease identification,
fruit classification, and germination rate monitoring. Seed
image analysis is considered a significant task for the
preservation of biodiversity and sustainability. This research
uses MobileNetV2, a deep learning convolutional neural
network (DCNNs) for seed classification. This model has been
preferred due to its simple architecture and memory-efficient
characteristics. A total of 14 different classes of seeds were used
for the experimentation. The results indicate accuracies of 98%
and 95% on training and test sets, respectively. |
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