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

Shenglian Lu, Zhen Song, Wenkang Chen, Tingting Qian, Yingyu Zhang, Ming Chen and Guo Li
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Shenglian Lu: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Zhen Song: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Wenkang Chen: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Tingting Qian: Agricultural Information Institutes of Science and Technology, Shanghai Academy of Agriculture Sciences, Shanghai 201403, China
Yingyu Zhang: Agricultural Information Institutes of Science and Technology, Shanghai Academy of Agriculture Sciences, Shanghai 201403, China
Ming Chen: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China
Guo Li: Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China

Agriculture, 2021, vol. 11, issue 10, 1-16

Abstract: 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.

Keywords: deep learning; plant phenotyping; leaf detection; object recognition (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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