使用深度神经网络联合模型在室外田间和室内实验室进行大豆豆荚和种子计数
Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks
AI总结：
本研究开发了高效的深度学习模型，通过结合YOLO-SAM和YOLO-DA提升室外田间大豆豆荚和种子计数的鲁棒性，并利用Mask-RCNN-Swin在合成数据上训练实现室内实验室的高精度计数，显著加速大豆育种过程。
摘要：
在室外田间自动计数大豆豆荚和种子可以在收获前快速估算产量，而室内实验室计数提供更高的准确性。两种方法都能显著加速育种过程。然而，在室外田间准确计数豆荚和种子仍然具有挑战性，且实验室计数仍缺乏足够准确的工具。在本研究中，我们开发了高效的深度学习模型，用于在室外田间和室内实验室计数大豆豆荚和种子。对于室外田间，不仅标注可见种子，还标注被遮挡的种子，使YOLO具备估计被遮挡大豆种子数量的能力。此外，我们通过将YOLO架构与HQ-SAM集成（YOLO-SAM）以及结合领域自适应技术（YOLO-DA），提升了模型在室外田间拍摄的大豆图像上的鲁棒性和泛化能力。在室外田间大豆图像上的测试中，豆荚计数的平均绝对误差（MAE）为6.13，种子计数为10.05。对于室内环境，我们使用补充了Swin Transformer模块的Mask-RCNN（Mask-RCNN-Swin），模型仅在由少量标注数据生成的合成训练图像上进行训练。该方法实现了近乎完美的准确度，在两项不同研究的实际实验室图像中，豆荚计数的MAE为1.07，种子计数为1.33。
英文摘要：
Automatic counting soybean pods and seeds in outdoor fields allows for rapid yield estimation before harvesting, while indoor laboratory counting offers greater accuracy. Both methods can significantly accelerate the breeding process. However, it remains challenging for accurately counting pods and seeds in outdoor fields, and there are still no accurate enough tools for counting pods and seeds in laboratories. In this study, we developed efficient deep learning models for counting soybean pods and seeds in both outdoor fields and indoor laboratories. For outdoor fields, annotating not only visible seeds but also occluded seeds makes YOLO have the ability to estimate the number of soybean seeds that are occluded. Moreover, we enhanced YOLO architecture by integrating it with HQ-SAM (YOLO-SAM), and domain adaptation techniques (YOLO-DA), to improve model robustness and generalization across soybean images taken in outdoor fields. Testing on soybean images from the outdoor field, we achieved a mean absolute error (MAE) of 6.13 for pod counting and 10.05 for seed counting. For the indoor setting, we utilized Mask-RCNN supplemented with a Swin Transformer module (Mask-RCNN-Swin), models were trained exclusively on synthetic training images generated from a small set of labeled data. This approach resulted in near-perfect accuracy, with an MAE of 1.07 for pod counting and 1.33 for seed counting across actual laboratory images from two distinct studies.
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