Plant phenotyping is the assessment of plant traits, which are influenced by genetics of the plant variety and the environmental in which it grows. These traits can be important indicators for evaluating particular new seed varieties or agricultural products for different environmental conditions. Through the MAPEO platform, we have created a functional and easy-to-use workflow for phenotyping crops and the analysis of crop traits throughout the growing season. As phenotyping requires identifying and analyzing traits of interest, MAPEO offers different crop indices, biomass and height products, as well as AI-driven counting algorithms to help crop breeders analyze plots for both seed development and production.
The MAPEO products, specific for phenotyping are:
| Product name | Product container | Type | Source | Description | Unit |
| ortho | Base | Raster | RGB | Orthomosaic, providing visual overview of the location | N/A |
| ortho-ms | Base | Raster | MSP | Orthomosaic, providing visual overview of the location | |
| dsm | Base | Raster | RGB | Digital Surface Model, including both the elevation of the soil and plant | Meter |
| dsm-ms | Base | Raster | MSP | Digital Surface Model, including both the elevation of the soil and plant | Meter |
| dtm | Base | Raster | RGB | Digital Terrain Model of the bare soils of the location, removing the elevation of the plants. Bare soil or low elevation grass needs to be present between the plots to create a good quality dtm. | Meter |
| dtm-ms | Base | Raster | MSP | Digital Terrain Model of the bare soils of the location, removing the elevation of the plants. Bare soil or low elevation grass needs to be present between the plots to create a good quality dtm. | |
| mask | mask | Raster | RGB | Mask based on the crop cover product | 0 (soil) or 255 (vegetation) |
| mask-ms | mask | Raster | MSP | Mask based on the crop cover-ms product | 0 (soil) or 255 (vegetation) |
| cover | map | Raster | RGB | Classification product which gives an indication of percentage crop coverage per pixel | %plant cover |
| cover-ms | map | Raster | MSP | Classification product which gives an indication of percentage crop coverage per pixel | %plant cover |
| plantheight | map | Raster | RGB | Plantheight calculated based on the dsm product and the dtm or dtm-ms on the first timepoint of that region. | meter |
| plantheight-ms | map | Raster | MSP | Plantheight calculated based on the dsm-ms product and the dtm or dtm-ms on the first timepoint of that region | meter |
| powdery-mildew | map | Raster | RGB | Classification of powdery mildew infected leaves | N/A |
| yellowindex | map | Raster | RGB | Index on a scale of 0-1 of how yellowness | N/A |
| cigreen | map | Raster | MSP | Chlorofyle Index based on the NIR and green spectral band, used to calculate chlorofyl content in the leaves | N/A |
| cirededge | map | Raster | MSP | Chlorofyle Index based on the NIR and RedEdge spectral band, used to calculate chlorofyl content in the leaves | N/A |
| mcari | map | Raster | MSP | MCARI gives a measure of the depth of chlorophyll absorption and is very sensitive to variations in chlorophyll concentrations as well as variations in Leaf Area Index (LAI). | N/A |
| ndre | map | Raster | MSP | The Normalized Difference Red Edge index is a modification of the traditional broadband NDVI. This VI differs from the NDVI by using bands along the red edge, instead of the main absorption and reflectance peaks. The NDRE capitalizes on the sensitivity of the vegetation red edge to small changes | N/A |
| ndvi | map | Raster | MSP | The Normalized Difference Vegetation Index (NDVI) is one of the oldest, most well-known, and most frequently used vegetation index. The combination of its normalized difference formulation and use of the highest absorption and reflectance regions of chlorophyll make it robust over a wide range of conditions. It can, however, saturate in dense vegetation conditions when LAI becomes high. | N/A |
| psri | map | Raster | MSP | The Plant Senescence Reflectance Index is designed to maximize the sensitivity of the index to the ratio of bulk carotenoids (for example, alpha-carotene and beta-carotene) to chlorophyll. An increase in PSRI indicates increased canopy stress (carotenoid pigment), the onset of canopy senescence, and plant fruit ripening. | N/A |
| tdvi | map | raster | TRM | Temperature map of the field | Degrees (Celsius) |
| count | ai | Vector | RGB | Count product is generated by an object detection or object segmentation algorithm. It identifies the location and the sizes of plant or plant organs (wheat ears, fruits, flowers, etc… Multiple object types per product is supported | N/A |
| ortho-skysat | satellite | Raster | Skysat | Overview image based on planet - skysat satellite data | N/A |
| ndvi-skysat | satellite | Raster | Skysat | Ndvi product based on planet - skysat satellite data | N/A |
Products are generally assembled in product bundles which can be ordered in the ordering workflow. Following product bundles are available.
| Product bundle name | Sensor type | Available Image resolution (mm) | Product list | Remarks |
| Basemap RGB | RGB | 1-2-10 | Ortho/dsm | |
| Crop biomass - RGB | RGB | 2-5-10 |
Ortho/dsm/dtm/cover/mask/ plantheight |
|
| Crop biomass - MSP | MSP | 10-20-30-50 |
Ortho-ms/dsm-ms/dtm-ms/ cover-ms/mask-ms/plantheight-ms |
|
| Crop Health | MSP | 10-20-30-50 |
Ortho-ms/ dsm-ms/dtm-ms/ ndvi/ndre/cigreen/cirededge/ mcari/psri |
|
| Powdery Mildew | RGB | 2 | Ortho/cover/mask/powdery-mildew | |
| Crop Objects - Ear | RGB | 1 | Ortho/Count | Available for wheat |
| Crop Objects - Plant | RGB | 2-5 | Ortho/Count/cover/mask | Available for sugar beet, lettuce, pumpkin, broccoli, maize |
| Crop Objects – Flower head | RGB | 2 | Ortho/Count | Available for onion, strawberry |
| Crop Objects – Fruit | RGB | 2 | Ortho/Count | Available for broccoli, tomato, pumpkin |
| Crop thermal | TRM | 50 | tdvi |
For different product bundles you have the choice to a specific image resolution, which may vary according to crop type, growing stage, and desired measurement precision. We advise to keep the amount of drone flights per timepoint restricted to 1 or 2. For instance, if you are interested in crop biomass and crop health at a certain timepoint, select a “crop biomass – msp” and “crop health” product with the same resolution. This will keep the amount of drone flights to a minimum.