Lu Yuchun, Lu Xiaoyi, Zheng Liping, Sun Min, Chen Siyu, Chen Baiyan, Wang Tong, Yang Jiming, Lv Chunli
China Agricultural University, Beijing 100083, China.
Plants (Basel). 2024 Mar 28;13(7):972. doi: 10.3390/plants13070972.
In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method's effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method's capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.
在本研究中，提出了一种基于多模态数据和变压器模型的创新方法，以应对农业病害检测和问答系统中的挑战。该方法有效地整合了图像、文本和传感器数据，利用深度学习技术对复杂的农业相关问题进行深入分析和处理。该研究取得了技术突破，为智能农业的发展提供了新的视角和工具。在农业病害检测任务中，所提出的方法表现出色，精确率、召回率和准确率分别达到0.95、0.92和0.94，显著优于其他传统深度学习模型。这些结果表明该方法在识别和准确分类各种农业病害方面的有效性，尤其在处理细微特征和复杂数据方面表现出色。在从农业图像生成描述性文本的任务中，该方法也表现出令人印象深刻的性能，精确率、召回率和准确率分别为0.92、0.88和0.91。这表明该方法不仅能够深入理解农业图像的内容，还能生成准确且丰富的描述性文本。目标检测实验进一步验证了我们方法的有效性，该方法在目标检测实验中的精确率、召回率和准确率分别为0.96、0.91和0.94。这一成果突出了该方法在准确定位和识别农业目标方面的能力，尤其是在复杂环境中。总体而言，本研究中的方法不仅在农业病害检测、图像字幕生成和目标检测等多项任务中表现卓越，还展示了多模态数据和深度学习技术在智能农业应用中的巨大潜力。
