Counting Dense Leaves under Natural Environments via an Improved Deep-Learning-Based Object Detection Algorithm

文献类型： 外文期刊

第一作者： Lu, Shenglian

作者： Lu, Shenglian;Song, Zhen;Chen, Wenkang;Chen, Ming;Li, Guo;Qian, Tingting;Zhang, Yingyu

作者机构：

关键词： deep learning; plant phenotyping; leaf detection; object recognition

期刊名称：AGRICULTURE-BASEL （ 影响因子：2.925； 五年影响因子：3.044 ）

ISSN：

年卷期： 2021 年 11 卷 10 期

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收录情况： SCI

摘要： 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 smooth(L1) 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.

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