High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges
High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HT...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-03-01
|
| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000029 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595323350941696 |
|---|---|
| author | Tao Cheng Dongyan Zhang Gan Zhang Tianyi Wang Weibo Ren Feng Yuan Yaling Liu Zhaoming Wang Chunjiang Zhao |
| author_facet | Tao Cheng Dongyan Zhang Gan Zhang Tianyi Wang Weibo Ren Feng Yuan Yaling Liu Zhaoming Wang Chunjiang Zhao |
| author_sort | Tao Cheng |
| collection | DOAJ |
| description | High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years. This paper reviews the unique aspects and research priorities in forage phenotypic monitoring, highlights key remote sensing platforms, examines the applications of advanced sensing technology for quantifying phenotypic traits, explores artificial intelligence (AI) algorithms in phenotypic data integration and analysis, and assesses recent progress in phenotypic genomics. The practical applications of HTP technology in forage remain constrained by several challenges. These include establishing uniform data collection standards, designing effective algorithms to handle complex genetic and environmental interactions, deepening the cross-exploration of phenomics-genomics, solving the problem of pathological inversion of forage phenotypic growth monitoring models, and developing low-cost forage phenotypic equipment. Resolving these challenges will unlock the full potential of HTP, enabling precise identification of superior forage traits, accelerating the breeding of superior varieties, and ultimately improving forage yield. |
| format | Article |
| id | doaj-art-4e0901da2b17496eab66d2c232e6ee5d |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-4e0901da2b17496eab66d2c232e6ee5d2025-01-19T06:26:32ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-03-0115198115High-throughput phenotyping techniques for forage: Status, bottleneck, and challengesTao Cheng0Dongyan Zhang1Gan Zhang2Tianyi Wang3Weibo Ren4Feng Yuan5Yaling Liu6Zhaoming Wang7Chunjiang Zhao8College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China; National Center of Pratacultural Technology Innovation (under preparation), Hohhot 010000, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China; National Center of Pratacultural Technology Innovation (under preparation), Hohhot 010000, China; Corresponding author at: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China.National Center of Pratacultural Technology Innovation (under preparation), Hohhot 010000, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaSchool of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaNational Center of Pratacultural Technology Innovation (under preparation), Hohhot 010000, ChinaNational Center of Pratacultural Technology Innovation (under preparation), Hohhot 010000, ChinaNational Center of Pratacultural Technology Innovation (under preparation), Hohhot 010000, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding author.High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years. This paper reviews the unique aspects and research priorities in forage phenotypic monitoring, highlights key remote sensing platforms, examines the applications of advanced sensing technology for quantifying phenotypic traits, explores artificial intelligence (AI) algorithms in phenotypic data integration and analysis, and assesses recent progress in phenotypic genomics. The practical applications of HTP technology in forage remain constrained by several challenges. These include establishing uniform data collection standards, designing effective algorithms to handle complex genetic and environmental interactions, deepening the cross-exploration of phenomics-genomics, solving the problem of pathological inversion of forage phenotypic growth monitoring models, and developing low-cost forage phenotypic equipment. Resolving these challenges will unlock the full potential of HTP, enabling precise identification of superior forage traits, accelerating the breeding of superior varieties, and ultimately improving forage yield.http://www.sciencedirect.com/science/article/pii/S2589721725000029ForageHigh-throughput phenotypingPrecision identificationSensorsArtificial intelligenceEfficient breeding |
| spellingShingle | Tao Cheng Dongyan Zhang Gan Zhang Tianyi Wang Weibo Ren Feng Yuan Yaling Liu Zhaoming Wang Chunjiang Zhao High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges Artificial Intelligence in Agriculture Forage High-throughput phenotyping Precision identification Sensors Artificial intelligence Efficient breeding |
| title | High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges |
| title_full | High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges |
| title_fullStr | High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges |
| title_full_unstemmed | High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges |
| title_short | High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges |
| title_sort | high throughput phenotyping techniques for forage status bottleneck and challenges |
| topic | Forage High-throughput phenotyping Precision identification Sensors Artificial intelligence Efficient breeding |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000029 |
| work_keys_str_mv | AT taocheng highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT dongyanzhang highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT ganzhang highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT tianyiwang highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT weiboren highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT fengyuan highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT yalingliu highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT zhaomingwang highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges AT chunjiangzhao highthroughputphenotypingtechniquesforforagestatusbottleneckandchallenges |