WheatAI v1.0: An AI-Powered High Throughput Wheat Phenotyping Platform
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918288129261568 |
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| author | Maimaitijiang, Maitiniyazi Ghimire, Hillson Thapa, Subash Billah, Mohammad Maruf Sehgal, Shaurya Singh, Mandeep Kaushal, Swas Poudel, Kushal Subedi, Santosh Janjua, Ubaid Ur Rehman Makonga, Lise-Olga Halder, Jyotirmoy Gill, Harsimardeep S. Sher, Mazhar Sidhu, Jagdeep Singh Sehgal, Sunish K. |
| author_facet | Maimaitijiang, Maitiniyazi Ghimire, Hillson Thapa, Subash Billah, Mohammad Maruf Sehgal, Shaurya Singh, Mandeep Kaushal, Swas Poudel, Kushal Subedi, Santosh Janjua, Ubaid Ur Rehman Makonga, Lise-Olga Halder, Jyotirmoy Gill, Harsimardeep S. Sher, Mazhar Sidhu, Jagdeep Singh Sehgal, Sunish K. |
| contents | High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08863 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WheatAI v1.0: An AI-Powered High Throughput Wheat Phenotyping Platform Maimaitijiang, Maitiniyazi Ghimire, Hillson Thapa, Subash Billah, Mohammad Maruf Sehgal, Shaurya Singh, Mandeep Kaushal, Swas Poudel, Kushal Subedi, Santosh Janjua, Ubaid Ur Rehman Makonga, Lise-Olga Halder, Jyotirmoy Gill, Harsimardeep S. Sher, Mazhar Sidhu, Jagdeep Singh Sehgal, Sunish K. Other Computer Science High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale. |
| title | WheatAI v1.0: An AI-Powered High Throughput Wheat Phenotyping Platform |
| topic | Other Computer Science |
| url | https://arxiv.org/abs/2601.08863 |