Artificial intelligence for automated identification of cannabaceae family plant diseases and gender on low-resource devices

Automated and early identification of plant diseases from their leaves is an important task in agriculture and can have positive impacts on crop yield and quality. Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and have a direct impact on food security and nutrition worldwide. Due to the wide variety of crops and diseases, even a farmer or pathologist can often fail to identify plant diseases by visualizing the affected leaves. However, visual observation remains the primary approach to disease identification. With the advances in artificial intelligence (AI) and mobile technologies in recent years, it is becoming possible to develop an embedded solution using machine learning that is accessible to farmers on their cell phones. In this project, we propose to develop and optimize deep learning models to detect diseases and gender for plantsin the cannabaceae family.

Intern: 
Adnane Ait Nasser;Mohamed Chetoui
Superviseur universitaire: 
Moulay Akhloufi
Province: 
New Brunswick
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