A research team developed the Point-Line Net, a deep learning method based on the Mask R-CNN framework, to automatically recognize maize field images and determine the number and growth trajectory of leaves and stalks. The model achieved an object detection accuracy (mAP50) of 81.5% and introduced a new lightweight keypoint detection branch. This innovative method promises to enhance the efficiency of plant breeding and phenotype detection in complex field environments, paving the way for more accurate crop management and yield prediction.
Maize, a vital crop globally, is essential for food, feed, and industrial applications. Understanding maize phenotypes, such as plant height, leaf number, and length, is essential for increasing yield and precision breeding. Despite advances in computer vision and deep learning, accurate phenotypic detection in field conditions remains challenging due to complex backgrounds and environmental factors. Current methods, mostly designed for controlled environments, struggle with these challenges.
A study (DOI: 10.34133/plantphenomics.0199) published in Plant Phenomics on 29 May 2024, proposes the Point-Line Net model to improve field phenotypic detection by accurately locating and tracking maize leaf positions and trajectories.
In this study, the research assessed the object detection accuracy for maize using three popular models: Faster R-CNN, RetinaNet, and YOLOv3. Using the original model architectures, it was found that Faster R-CNN with ResNet101 + FPN achieved the highest performance, with an mAP50 of 76.2% and an mAP75 of 39.9%, albeit with a longer detection time of 89.6 ms. To enhance accuracy, hyperparameters were fine-tuned, and Soft-NMS and D IoU techniques were incorporated, improving mAP50 to 75.5% and mAP75 to 49.2%. Inspired by human keypoint detection, the research developed the innovative Point-Line Net model, which achieved an mAP50 of 81.5% and an mAP75 of 50.1%, outperforming traditional methods. This method also demonstrated better accuracy in describing leaf and stalk trajectories, with a custom distance evaluation index (mLD) of 33.5, indicating its effectiveness in complex field environments. The training and validation process revealed that the model stabilized around the 100th epoch, suggesting optimal performance for subsequent prediction tasks.
According to the study’s lead researcher, Jue Ruan, “We believe that the results of this study can also provide ideas for field management and phenotypic data collection for other crops.”
In summary, the Point-Line Net model achieved an object detection accuracy (mAP50) of 81.5% and introduced a new lightweight keypoint detection branch, significantly improving phenotypic detection. The research highlights the potential of deep learning methods to enhance the efficiency of field plant phenotyping, offering valuable insights for future crop breeding and management. Integrating additional annotation information, such as specific growth stages and multi-angle data, could further enhance model accuracy and applicability, paving the way for more precise agricultural practices and better crop yield predictions.
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References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0199
Authors
Bingwen Liu1,2,†, Jianye Chang2,†, Dengfeng Hou1,2, Yuchen Pan1,2, Dengao Li1,*, Jue Ruan2,**
Affications
1 College of Computer Science and Technology (College of Data Science), Taiyuan University ofTechnology, Taiyuan, 030024, Shanxi, China.
2 Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome AnalysisLaboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute atShenzhen, Chinese Academy of Agricultural Sciences, 518120, Shenzhen, China.
* Address correspondence to: lidengao@tyut.edu.cn.
** Address correspondence to: ruanjue@caas.cn.
† These authors contributed equally to this work.
Funding information
This work was supported by the Project on Genome Refinement of Key Model Organism andits Demonstration and Application-Subtopic 1 (2022YFC3400300), the Acquisition and Decodingof Current Signals for Biological Nanopore Sequencing-Subtopic (2019YFA0707003), and theAgricultural Science and Technology Innovation Program.
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal’s Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
