Accurate identification of crop growth stages is a crucial basis for implementing effective cultivation management. With the development of deep learning techniques in image understanding, research on intelligent real-time recognition of crop growth stages based on RGB images has garnered significant attention. However, the small differences and high similarity in crop morphological characteristics during the transition between adjacent growth stages pose challenges for accurate identification. To address this issue, this study proposes a multi-scale convolutional neural network model, termed MultiScalNet-Wheat (MSN-W), which enhances the algorithm's ability to learn complex features by utilizing multi-scale convolution and attention mechanisms. This model extracts key information from redundant data to identify winter wheat growth stages in complex field environments. Experimental results show that the MSN-W model achieves a recognition accuracy of 97.6 %, outperforming typical convolutional neural network models such as VGG19, ResNet50, MobileNetV3, and DenseNet. To further address the difficulty in recognizing growth stages during transition periods, where canopy morphological features are highly similar and show small differences, this paper introduces an innovative approach by incorporating sequential environmental data related to wheat growth stages. By extracting these features and performing multi-modal collaborative inference, a multi-modal feature-based wheat growth stage recognition model, termed MultiModalNet-Wheat (MMN-W), is constructed on the basis of the MSN-W model. Experimental results indicate that the MMN-W model achieves a recognition accuracy of 98.5 %, improving by 0.9 % over the MSN-W model. Both the MSN-W and MMN-W models provide accurate methods for observing wheat growth stages, thereby supporting the scientific management of winter wheat at different growth stages.

中文翻译：

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基于多模态数据的小麦生育期识别方法


准确识别作物生长阶段是实施有效栽培管理的重要基础。随着深度学习技术在图像理解方面的发展，基于 RGB 图像的作物生长阶段智能实时识别研究引起了广泛关注。然而，在相邻生长阶段之间的过渡过程中，作物形态特征的微小差异和高度相似性对准确鉴定提出了挑战。为了解决这个问题，本研究提出了一种多尺度卷积神经网络模型，称为 MultiScalNet-Wheat （MSN-W），它通过利用多尺度卷积和注意力机制增强了算法学习复杂特征的能力。该模型从冗余数据中提取关键信息，以识别复杂田间环境中的冬小麦生长阶段。实验结果表明，MSN-W 模型的识别准确率达到 97.6%，优于 VGG19、ResNet50、MobileNetV3 和 DenseNet 等典型卷积神经网络模型。为了进一步解决在过渡期识别生长阶段的困难，其中冠层形态特征高度相似且差异很小，本文通过结合与小麦生长阶段相关的连续环境数据，引入了一种创新方法。通过提取这些特征并进行多模态协同推理，在 MSN-W 模型的基础上构建了一种基于多模态特征的小麦生长阶段识别模型，称为 MultiModalNet-Wheat （MMN-W）。实验结果表明，MMN-W 模型的识别准确率达到 98.5 %，比 MSN-W 模型提高了 0.9 %。 MSN-W 和 MMN-W 模型都为观察小麦生长阶段提供了准确的方法，从而支持不同生长阶段冬小麦的科学管理。