H2YOLO: plant water status from cellular to whole plant level
Lead Research Organisation:
University of Nottingham
Department Name: Sch of Biosciences
Abstract
Climate change projections indicate increased warming and drying trends for the upcoming decades. As a result, water availability is expected to become increasingly limiting for our agricultural systems. During the process of photosynthesis, plants use pores on the surface of their leaves to take carbon dioxide from the surrounding environment. However, at the same time, water is lost through the process of evapotranspiration. Water use efficiency (WUE) refers to the balance between these two processes. Optimising WUE is a key target for crop improvement, with the need to reduce water inputs (i.e. irrigation) to our cropping systems.
Water impacts plant growth across all scales; from cellular to whole plant level. At the cellular scale, stomata control the exchange of gases, whilst physical structures and intercellular channels control water movement through plant tissues. The number and shape of the stomata therefore determine the gas exchange capacity. When water exits the leaf, the turgor pressure within the plant tissue changes, causing the leaf to change shape. For example, under low water availability, a leaf may droop or wilt. Therefore, water loss is able to alter whole plant structure, and, conversely, quantifying these structural changes can be used to infer water use.
At present, rapid and reliable methods to determine cross-scale plant water use are lacking. This will be overcome within this project through the application of deep learning: computational methods that enable a computer to be 'taught' to complete a task, in this case identifying and characterising structural features of plant. By partnering researchers from the UK at the University of Nottingham and the University of Essex, with researchers in Australia at the University of Adelaide, we will explore how plants respond to water availability in diverse environments.
The aims of H2YOLO are to:
1. Use handheld microscopes and cameras to capture photos and videos of plants, focusing on cellular structures, leaves and whole plants
2. Determine how stomata are distributed across a leaf surface by using deep learning for detection
3. Quantify how transpiration impacts leaf movement by detecting and tracking leaf movement
4. Compare water use across contrasting plant species, including the model plant Arabidopsis thaliana and commercial varieties of grapevine (Vitis vinifera),
To achieve this requires developments in a number of key areas. This includes strategies for capturing photos and videos of plants, as well as improvements to the deep learning process.
Through this project, we will showcase and advance the application of AI (i.e. deep learning) methods for bioscience disciplines. We will produce new datasets and analysis pipelines that support biological and computational research, which will be shared publicly. Ultimately, this research will support the identification of plants with improved WUE, and thus enable improvements to crop yields under climate change.
Water impacts plant growth across all scales; from cellular to whole plant level. At the cellular scale, stomata control the exchange of gases, whilst physical structures and intercellular channels control water movement through plant tissues. The number and shape of the stomata therefore determine the gas exchange capacity. When water exits the leaf, the turgor pressure within the plant tissue changes, causing the leaf to change shape. For example, under low water availability, a leaf may droop or wilt. Therefore, water loss is able to alter whole plant structure, and, conversely, quantifying these structural changes can be used to infer water use.
At present, rapid and reliable methods to determine cross-scale plant water use are lacking. This will be overcome within this project through the application of deep learning: computational methods that enable a computer to be 'taught' to complete a task, in this case identifying and characterising structural features of plant. By partnering researchers from the UK at the University of Nottingham and the University of Essex, with researchers in Australia at the University of Adelaide, we will explore how plants respond to water availability in diverse environments.
The aims of H2YOLO are to:
1. Use handheld microscopes and cameras to capture photos and videos of plants, focusing on cellular structures, leaves and whole plants
2. Determine how stomata are distributed across a leaf surface by using deep learning for detection
3. Quantify how transpiration impacts leaf movement by detecting and tracking leaf movement
4. Compare water use across contrasting plant species, including the model plant Arabidopsis thaliana and commercial varieties of grapevine (Vitis vinifera),
To achieve this requires developments in a number of key areas. This includes strategies for capturing photos and videos of plants, as well as improvements to the deep learning process.
Through this project, we will showcase and advance the application of AI (i.e. deep learning) methods for bioscience disciplines. We will produce new datasets and analysis pipelines that support biological and computational research, which will be shared publicly. Ultimately, this research will support the identification of plants with improved WUE, and thus enable improvements to crop yields under climate change.
