Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery
The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.
Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery
The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.
Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery
Jingming Wu (Autor:in) / Xu Li (Autor:in) / Ziyan Shi (Autor:in) / Senwei Li (Autor:in) / Kaiyao Hou (Autor:in) / Tiecheng Bai (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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