Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China.
Huanghuaihai Key Laboratory of Intelligent Agricultural Technology, Ministry of Agriculture and Rural Areas, Zhengzhou, 450002, China.
Sci Rep. 2024 Nov 29;14(1):29676. doi: 10.1038/s41598-024-80244-1.
The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments.
小麦穗部图像的自动检测和计数对产量预测和品种评价具有重要意义。因此，准确、及时地估计穗数对于小麦生产至关重要。然而，在实际生产中，由于小麦穗部图像易受光照条件、拍摄角度、遮挡和重叠等因素的影响，小麦穗部的轮廓和特征不清晰，这影响了小麦穗部自动检测和计数的准确性。为了解决上述问题，进一步提高小麦穗部计数的准确性，本文提出了一种基于现有目标对象计数模型 DM-Count 增强局部上下文监督信息的改进小麦穗部计数模型 DMseg-Count。首先，引入了小麦穗部局部分割分支，以改进 DM-Count 的网络架构，从而提取小麦穗部的局部上下文监督信息。其次，设计了元素级别的点乘机制，以融合小麦穗部的全局和局部上下文监督信息。最后，构建了总损失函数来优化模型。实验结果表明，所提出的 DMseg-Count 模型的平均绝对误差（MAE）和均方根误差（RMSE）分别为 5.79 和 7.54，比 crowd counting（DM-Count）模型的标准分布匹配分别高 9.76 和 10.91。与其他深度学习模型相比，所提出的 DMseg-Count 模型可以在具有挑战性的情况下检测小麦穗部图像，具有更好的计算机视觉处理能力和性能评估检测效果。综上所述，所提出的 DMseg-Count 模型可以有效地检测小麦穗部，具有良好的计数性能，为复杂田间环境下小麦穗部的自动计数和产量预测提供了一种新方法。
