Academic Journal of Computing & Information Science, 2023, 6(2); doi: 10.25236/AJCIS.2023.060203.
Luo Yuchen1, Zan Xusen2, Li Jiali3, Zhang Xueting4
1College of Mathematical Statistics, Ningxia University, Yinchuan, China
2College of Physics and Electronic and Electrical Engineering, Ningxia University, Yinchuan, China
3College of Advanced Interdisciplinary Studies, Ningxia University, Zhongwei, China
4College of Education, Ningxia University, Yinchuan, China
This study endeavors to utilize a convolutional neural network (CNN) to categorize pictures of nitrogen insufficiency in rice foliage. Thousands of real photos of rice leaves falling under four different labels of nitrogen deficiency served as the basis for this classification model. This study compares the effectiveness of several optimizers on this dataset and provides a detailed description of how a convolutional neural network model is constructed. The accuracy and loss of the model are computed to quantitatively assess its performance. Finally, when the optimizer is switched from Adam to Nadam, the accuracy of the model increases from 94.75% to 99.5%.
Machine Learning, CNN, Data Processing, Image Classification, Optimizer Comparison
Luo Yuchen, Zan Xusen, Li Jiali, Zhang Xueting. Rice Leaf Nitrogen Deficiency Image Classification Model Based on CNN. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 2: 16-22. https://doi.org/10.25236/AJCIS.2023.060203.
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