Enhancing precision agriculture through AI-driven crop monitoring - BC-928

Genre de projet: Recherche
Discipline(s) souhaitée(s): Science de l'alimentation, Sciences de la vie, Informatique, Sciences mathématiques
Entreprise: CropVue
Durée du projet: 6 mois à 1 an
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): BC, Canada
Nombre de postes: 2
Niveau de scolarité désiré: CollègeÉtudes de premier cycle/baccalauréatMaîtriseDoctoratRecherche postdoctoraleNouvelle diplômée/nouveau diplômé
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: Yes

Au sujet de l’entreprise: 

CropVue is dedicated to empowering farmers with AI-powered automated scouting solutions that support decision making that results in improved agricultural and environmental outcomes. Through our advanced data analytics, daily monitoring, and AI-driven predictions, we help growing operations make informed decisions that drive grower success and foster environmental stewardship.

Veuillez décrire le projet.: 

CropVue is developing innovative Crop Monitor devices: advanced camera systems designed to automatically scout agricultural crops. These devices capture high-resolution images of crops at various stages of development, enabling farmers to identify key features that indicate when specific interventions are needed. For instance, in apple orchards, our monitors track essential phenological stages, including bud break, floral development, full bloom, fruit formation, and harvest readiness. By collecting daily imagery along with microclimate data, we create a comprehensive dataset linking visual crop features to grower interventions.
Building on our current pilot work funded by an AAFC grant, this project seeks to expand the scope of data collection and AI model development. The AAFC grant allowed us to validate the initial concept by deploying early prototypes on select farms. Now we aim to scale this pilot to additional sites to refine and optimize our AI algorithms.

The candidates would be responsible for the recommendation of ML framework and tools, development of 2 models that are able to forecast and recommend grower intervention timing.

Expertise ou compétences exigées: 

- Machine Learning

- AI