Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

• Published in: Agronomy (Basel) 2023-08, Vol.13 (8), p.1974
• Main Authors: Sun, Weizhong, Fu, Bohan, Zhang, Zhao
• Format: Article
• Language: English
• Subjects: Agricultural land; Agriculture; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Classification; Classifiers; Corn; data augmentation; Datasets; Deep learning; Drone aircraft; Fertilization; Fertilizers; Food industry; Learning algorithms; Localization; Machine learning; maize nitrogen detection; Methods; Model accuracy; Neural networks; Nitrogen; Precision farming; Proteins; Recall; Regression models; Remote sensing systems; UAV images; Unmanned aerial vehicles; Zoning
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13081974

Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substant