Predicting Perishable Field Vegetables’ Supply Using Artificial Intelligence and Satellite Imagery

Uncertainty at the upstream supply of agricultural products has been a challenge for growers. This uncertainty governs the market of non-strategic crops such as perishable field vegetables by affecting suppliers (or growers), distributors, and consumers. It causes too much or too little supply and consequently unexpected price variations. The supply chain of perishable food is a complex puzzle made up of many elements. Among these elements, this project is focused on estimating the area under harvest of specific crops using Satellite Imagery and Machine Learning. Satellite images provide a wealth of information for decision-making in agriculture. This project applies Computer Vision to analyze satellite images from the vegetable fields to evaluate the area under harvest. This analysis could illustrate the concentration of farms growing a specific vegetable locally.

Intern: 
Mahla Mirali
Faculty Supervisor: 
Hamidreza Mahyar
Province: 
Ontario
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