Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status

The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is l...

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Bibliographic Details
Published in:PloS one Vol. 17; no. 11; p. e0273360
Main Authors: Zhai, Qiang, Ye, Chun, Li, Shuang, Liu, Jizhong, Guo, Zhiming, Chang, Ruzhi, Hua, Jing
Format: Journal Article
Language:English
Published: United States Public Library of Science 22.11.2022
Public Library of Science (PLoS)
Subjects:
Accuracy
Agricultural land
Agricultural production
Algorithms
Artificial neural networks
Biology and Life Sciences
Cameras
Chlorophyll
Classification
Computer and Information Sciences
Convolution
Crop yield
Datasets
Deep learning
Evaluation
Experiments
Feature extraction
Fertilizers
Labor
Machine learning
Measurement
Medicine and Health Sciences
Methods
Modelling
Monitoring
Monitoring methods
Neural networks
Neural Networks, Computer
Nitrogen
Nitrogen in the body
Nutrition
Nutrition monitoring
Nutritional aspects
Nutritional Status
Oryza
R&D
Remote sensing
Research & development
Research and Analysis Methods
Rice
Rice yield
Varieties
China
Jiangxi China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0273360