[{"_path":"/tools/crop-monitoring","_draft":false,"_partial":false,"_locale":"en","_empty":false,"title":"The Farmer's Dashboard","description":"The Farmer's Dashboard and its associated device, the Cablebot, is our primary tool for crop monitoring. With the help of an aerial imaging device, images of the crop are collected and analysed to give the farmer an overview of the status of the crops and also of individual plants.","excerpt":{"type":"root","children":[{"type":"element","tag":"section-cover","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:title":""},"children":[{"type":"element","tag":"h1","props":{"id":"the-farmers-dashboard"},"children":[{"type":"text","value":"The Farmer's Dashboard"}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The Farmer's Dashboard is a farming tool that provides daily automated insights about your crops. It helps with mapping of crop bed, the location, and identification of individual plants, and the extraction of their growth curves from the collected data. It is a set of hardware and software tools for affordable, customisable, and high frequency crop monitoring."}]},{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"base-embed","props":{"id":"g5bjv3CTZ-8"},"children":[]}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/tools/crop-monitoring/1-farmers-dashboard.png"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The dashboard supports farmers with imaging and analytics that identify and track plant growth."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/tools/crop-monitoring/2-monitoring-segmentation.jpg"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"After plants are detected, a catalogue of individual plants is created. By comparing them with historical data, we can obtain plant growth curves. All of the information is then combined into a weed map which is made available on the Farmers Dashboard website."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/tools/crop-monitoring/3-cablebot.jpg"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Image data may be recieved from multiple sources, including drones, the rover, and cable bots. ROMI's Cable Bot is adapted for use in greenhouses, and polytunnels, and in situations where the use of a drone is not adapated."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/tools/crop-monitoring/4-monitoring-orthomosaic.jpg"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Once set up, the ROMI Cable Bot will move multiple times a day across the crop bed, taking high definition images and sending them to a ROMI server. The images are assembled into a unique portrait of your crop bed."}]}]}]},"img":"/assets/tools/crop-monitoring/0-cover.png","docs":"https://docs.romi-project.eu/Farmers%20Dashboard/","_type":"markdown","_id":"content:2.tools:2.crop-monitoring.md","_source":"content","_file":"2.tools/2.crop-monitoring.md","_extension":"md"},{"_path":"/research/modeling","_draft":false,"_partial":false,"_locale":"en","_empty":false,"title":"Virtual plants and AI","description":"We use virtual plants to train neural networks. This allows us to detect plant organs without the need of collecting and annotating field data.","excerpt":{"type":"root","children":[{"type":"element","tag":"section-cover","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:title":""},"children":[{"type":"element","tag":"h1","props":{"id":"plant-modeling-and-ai"},"children":[{"type":"text","value":"Plant modeling and AI"}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"We focus on the generation of synthetic ground truth data (images or point clouds) using virtual plant models, and on novel analysis techniques for 3D and 3D+time data."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/research/modeling/1-arabidopsis-model.jpg"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The work on the plant models has resulted in approved models for Arabidopsis thaliana and tomato plants. The model of A. thaliana was successfully used to train neural networks for the semantic segmentation of images of real plants and to produce high-quality point clouds for subsequent machine learning and analysis tasks."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/research/modeling/2-chenopodium-model.jpg"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"This motivates the work to go further into the realistic rendering of plants and improve the physical coherence of the generated 3D plants. Existing state-of-the-art models, including ours, do not correctly detect and handle intersecting organs, for example. This problem is currently investigated together with other key issues related to photo-realistic rendering of plants."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/research/modeling/3-skeletons-zoom.jpg"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"The challenge remains to robustly segment 3D plant data into its constituent organs can be tackled using several methods, from geometric methods to machine learning methods that use 2D image segmentation or 3D point cloud segmentation. An additional challenge is the precise extraction of the plant's skeleton from a 3D representation."}]}]},{"type":"element","tag":"section-split","props":{},"children":[{"type":"element","tag":"template","props":{"v-slot:media":""},"children":[{"type":"element","tag":"p","props":{},"children":[{"type":"element","tag":"img","props":{"alt":"dashboard screenshot","src":"/assets/research/modeling/4-arabidopsis.png"},"children":[]}]}]},{"type":"element","tag":"p","props":{},"children":[{"type":"text","value":"Tracking the plant growth over time raises the issue on the space-time registration of the collected 3D data. The combination of plant models and machine learning may help us predict the plant's shape ahead of time."}]}]}]},"img":"/assets/research/modeling/0-cover.png","_type":"markdown","_id":"content:3.research:1.modeling.md","_source":"content","_file":"3.research/1.modeling.md","_extension":"md"}]