Counting Dense Leaves under Natural Environments via an Improved Deep-Learning-Based Object Detection Algorithm
Counting Dense Leaves under Natural Environments via an Improved Deep-Learning-Based Object Detection Algorithm
复制标题
通过改进的基于深度学习的目标检测算法计算自然环境下的茂密树叶
DOI：
10.3390/agriculture11101003
复制
发表时间：
2021-10-01
影响因子：
3.6
通讯作者：
Li, Guo
中科院分区：
文献类型：
--
作者：
Lu, Shenglian;Song, Zhen;Li, Guo
叶片是植物光合作用和营养物质生产的关键器官;因此，叶片数量是描述冠层发育和生长的关键指标之一。叶片的不规则形状和分布，以及自然光的影响，使得叶片的分割和检测过程变得困难。植物表型参数获取不准确，可能影响后续对作物生长状况和作物产量的判断。为了解决自然环境下密集和重叠的植物叶片计数的挑战，我们提出了一种改进的基于深度学习的对象检测算法，通过将空间到深度模块，卷积块注意力模块（CBAM）和Atrous空间金字塔池（ASPP）合并到网络中，并应用smoothL 1函数来改进对象预测的损失函数。我们评估了我们的方法在室内和室外环境下收集的五种不同的植物物种的图像。实验结果表明，我们的算法，计数密集的树叶提高了85%至96%的平均检测准确率。我们的算法也表现出更好的性能，在检测精度和时间消耗相比，其他国家的最先进的目标检测算法。
The leaf is the organ that is crucial for photosynthesis and the production of nutrients in plants; as such, the number of leaves is one of the key indicators with which to describe the development and growth of a canopy. The irregular shape and distribution of the blades, as well as the effect of natural light, make the segmentation and detection process of the blades difficult. The inaccurate acquisition of plant phenotypic parameters may affect the subsequent judgment of crop growth status and crop yield. To address the challenge in counting dense and overlapped plant leaves under natural environments, we proposed an improved deep-learning-based object detection algorithm by merging a space-to-depth module, a Convolutional Block Attention Module (CBAM) and Atrous Spatial Pyramid Pooling (ASPP) into the network, and applying the smoothL1 function to improve the loss function of object prediction. We evaluated our method on images of five different plant species collected under indoor and outdoor environments. The experimental results demonstrated that our algorithm which counts dense leaves improved average detection accuracy of 85% to 96%. Our algorithm also showed better performance in both detection accuracy and time consumption compared to other state-of-the-art object detection algorithms.