Enhancing Jujube Forest Growth Estimation and Disease Detection Using a Novel Diffusion-Transformer Architecture
Author:
Hu Xiangyi1, Zhang Zhihao1, Zheng Liping1, Chen Tailai1, Peng Chao1, Wang Yilin1, Li Ruiheng1, Lv Xinyang1, Yan Shuo1ORCID
Affiliation:
1. China Agricultural University, Beijing 100083, China
Abstract
This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of large-scale and highly complex forest areas due to limitations in data processing capabilities and feature extraction precision. In response to this challenge, this paper designs and conducts a series of benchmark tests and ablation experiments to systematically evaluate and verify the performance of the proposed model across key performance metrics such as precision, recall, accuracy, and F1-score. Experimental results demonstrate that compared to traditional machine learning models like Support Vector Machines and Random Forests, as well as common deep learning models such as AlexNet and ResNet, the model proposed in this paper achieves a precision of 95%, a recall of 92%, an accuracy of 93%, and an F1-score of 94% in the task of disease detection in jujube forests, showing similarly superior performance in growth estimation tasks as well. Furthermore, ablation experiments with different attention mechanisms and loss functions further validate the effectiveness of parallel attention and parallel loss function in enhancing the overall performance of the model. These research findings not only provide a new technical path for forestry disease monitoring and health assessment but also contribute rich theoretical and experimental foundations for related fields.
Funder
Pinduoduo-China Agricultural University Research Fund
Reference55 articles.
1. A review of imaging techniques for plant disease detection;Singh;Artif. Intell. Agric.,2020 2. Ran, J., Guo, W., Hu, C., Wang, X., and Li, P. (2022). Adverse effects of long-term continuous girdling of jujube tree on the quality of jujube fruit and tree health. Agriculture, 12. 3. Li, Q., Ren, J., Zhang, Y., Song, C., Liao, Y., and Zhang, Y. (2023, January 9–13). Privacy-Preserving DNN Training with Prefetched Meta-Keys on Heterogeneous Neural Network Accelerators. Proceedings of the 2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA. 4. Plant diseases and pests detection based on deep learning: A review;Liu;Plant Methods,2021 5. Zhang, Y., Wa, S., Liu, Y., Zhou, X., Sun, P., and Ma, Q. (2021). High-accuracy detection of maize leaf diseases CNN based on multi-pathway activation function module. Remote Sens., 13.
|
|