Skaïgreen: An aerial drone and deep learning project for the counting and nitrogen deficiency detection of coconut trees
Date
2023-05
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Abstract
This capstone project involved the implementation of a drone system to obtain an aerial view
of a coconut farm, with the goal of using deep learning models to detect and count coconut
plants, as well as identify nitrogen-deficient plants. The YOLOv5 algorithm was used for object
detection, with a unique ID assigned to each plant for counting. A VGGNet convolutional
neural network was trained on a dataset of labeled coconut plant images to classify plants as
"healthy" or "unhealthy". The results of the project included an automated drone system that
could fly and capture footage with a single click, allowing for efficient counting of coconut
plants and detection of nitrogen-deficient plants.
Description
Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degrees in Computer Science, May 2023
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Applied Project