Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou, 450001, China.
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, China.
Sci Rep. 2024 Apr 6;14(1):8106. doi: 10.1038/s41598-024-58638-y.
Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.
利用深度学习技术进行麦穗检测和计数在精准农业应用中受到了广泛关注，例如小麦生长监测、产量估计和资源分配。然而，由于麦穗的大小、方向、外观、纵横比、密度以及成像条件的复杂性等固有变化，准确检测小而密集的麦穗仍然具有挑战性。为了解决这些挑战，我们提出了一种新的方法，称为定向特征金字塔网络（Oriented Feature Pyramid Network，OFPN），该方法专注于通过使用定向边界框来检测旋转的麦穗。为了方便开发和评估我们提出的方法，我们引入了一个新的数据集，称为旋转全局麦穗数据集（Rotated Global Wheat Head Dataset，RGWHD）。该数据集是通过手动将 GWHD 数据集的图像标注为定向边界框构建的。此外，我们将路径聚合和平衡特征金字塔网络（Path-aggregation and Balanced Feature Pyramid Network）集成到我们的架构中，以便从输入图像中有效地提取语义和位置信息。这是通过在多个尺度上利用特征融合技术实现的，从而增强了对小麦穗的检测能力。为了提高密集和重叠麦穗的定位和检测精度，我们使用 Soft-NMS 算法来过滤建议的边界框。实验结果表明，OFPN 模型的性能优越，在定向麦穗检测中达到了 85.77%的平均准确率，超过了其他六个最先进的模型。此外，我们观察到麦穗计数的准确性有了显著提高，达到了 93.97%。与 Faster R-CNN 方法相比，这一准确率提高了 3.12%。定性和定量结果都证明了 OFPN 模型在各种挑战性场景下准确定位和计数麦穗的有效性。
