Prediction of weeds growth

14/01/2023 all-day

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIFAT, Université de Tours
Durée : 4-6 mois
Contact :
Date limite de publication : 2023-01-14

Contexte :
This internship takes place inside the regional project DESHERB’ROB (https://desherbrob.insa- which aims at elaborating new robots for high precision E-agriculture. The robot should be able to localize precisely weeds and to remove them. The originality of the project relies on the use of data coming from drone images as well as temporal data to predict the growth of the weeds and to try to combine them to detect at the earliest the location of weeds apparition. The internship will be done in close relationship with a PhD student.

Sujet :
The main goal of this internship is to work on a neural network model taking as inputs local climatic data (temperature, humidity, light…), previous observations about weeds growth during the year, previous observations about geo-localization of weeds in the fields. The geo-localized predictions will be combined with recognition based on image analysis of drone (work of the PhD).

1 A state of the art will be made about neural networks methods for multivariate time series. A focus on transformers and attention mechanism will be done. At the same time, a literature review on grass growth prediction will be conducted, using [Guyet et al. 2022] as a starting point.
2 Data collection, cleaning and preparation will be done, based on known benchmarks as well as true data. Defining experimental protocol.
3 Based on 1, an architecture will be proposed and implemented. As a first step, the geo-localization will not be considered.
4 Learning of the model.
5 Evaluation of the prediction based on several criteria (detection, growth…)
6 Improvements and addition of geo-localization.
7 Documentation, reports and cleaning of the code to make it reusable (using Git)

Profil du candidat :
Academic level equivalent to a Master 2 in progress or Engineer in its 5th year, in computer science with courses in AI and machine learning

Formation et compétences requises :
– a good experience in data analysis and machine learning (in python) is required
– some knowledge and experiences in deep learning and associated tools will be highly considered
– curiosity and ability to communicate and share your progress and to make written reports
– ability to propose solutions
– autonomy and good organization skills

Adresse d’emploi :
Computer Science Lab of the Université de Tours (LIFAT), Pattern Recognition and Image Analysis Team (RFAI)
64 av. Jean Portalis,
37200 Tours, France

Document attaché : 202301141932_stage DESHERBROB.pdf