1
|
Du J, Li J, Fan J, Gu S, Guo X, Zhao C. Detection and Identification of Tassel States at Different Maize Tasseling Stages Using UAV Imagery and Deep Learning. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0188. [PMID: 38933805 PMCID: PMC11200267 DOI: 10.34133/plantphenomics.0188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/19/2024] [Indexed: 06/28/2024]
Abstract
The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation. Existing tassel detection models are primarily used to identify mature tassels with obvious features, making it difficult to accurately identify small tassels or detasseled plants. This study presents a novel approach that utilizes unmanned aerial vehicles (UAVs) and deep learning techniques to accurately identify and assess tassel states, before and after manually detasseling in maize hybridization fields. The proposed method suggests that a specific tassel annotation and data augmentation strategy is valuable for substantial enhancing the quality of the tassel training data. This study also evaluates mainstream object detection models and proposes a series of highly accurate tassel detection models based on tassel categories with strong data adaptability. In addition, a strategy for blocking large UAV images, as well as improving tassel detection accuracy, is proposed to balance UAV image acquisition and computational cost. The experimental results demonstrate that the proposed method can accurately identify and classify tassels at various stages of detasseling. The tassel detection model optimized with the enhanced data achieves an average precision of 94.5% across all categories. An optimal model combination that uses blocking strategies for different development stages can improve the tassel detection accuracy to 98%. This could be useful in addressing the issue of missed tassel detections in maize hybridization fields. The data annotation strategy and image blocking strategy may also have broad applications in object detection and recognition in other agricultural scenarios.
Collapse
Affiliation(s)
- Jianjun Du
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plants,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Jinrui Li
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plants,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Information Engineering,
Northwest A&F University, Yangling, Shanxi 712100 China
| | - Jiangchuan Fan
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plants,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shenghao Gu
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plants,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xinyu Guo
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plants,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Chunjiang Zhao
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- College of Information Engineering,
Northwest A&F University, Yangling, Shanxi 712100 China
| |
Collapse
|
2
|
Zhang W, Wu S, Wen W, Lu X, Wang C, Gou W, Li Y, Guo X, Zhao C. Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning. PLANT METHODS 2023; 19:76. [PMID: 37528454 PMCID: PMC10394845 DOI: 10.1186/s13007-023-01051-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 07/10/2023] [Indexed: 08/03/2023]
Abstract
BACKGROUND The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits because of the advances in the point cloud deep learning method. However, this method requires a large number of data sets and is not robust to automatic segmentation of highly adherent organ components; thus, it should be combined with point cloud processing technology. RESULTS An innovative method of incomplete annotation of point cloud data was proposed for easy development of the dataset of maize tassels,and an automatic maize tassel phenotype analysis system: MaizeTasselSeg was developed. The tip feature of point cloud is trained and learned based on PointNet + + network, and the tip point cloud of tassel branch was automatically segmented. Complete branch segmentation was realized based on the shortest path algorithm. The Intersection over Union (IoU), precision, and recall of the segmentation results were 96.29, 96.36, and 93.01, respectively. Six phenotypic traits related to morphological structure (branch count, branch length, branch angle, branch curvature, tassel volume, and dispersion) were automatically extracted from the segmentation point cloud. The squared correlation coefficients (R2) for branch length, branch angle, and branch count were 0.9897, 0.9317, and 0.9587, respectively. The root mean squared error (RMSE) for branch length, branch angle, and branch count were 0.529 cm, 4.516, and 0.875, respectively. CONCLUSION The proposed method provides an efficient scheme for high-throughput organ segmentation of maize tassels and can be used for the automatic extraction of phenotypic traits of maize tassel. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.
Collapse
Affiliation(s)
- Wenqi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China
| | - Sheng Wu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Weiliang Wen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Xianju Lu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Chuanyu Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Wenbo Gou
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Yuankun Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Xinyu Guo
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
| |
Collapse
|
3
|
Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
Collapse
Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
| |
Collapse
|