Fall armyworm detection in Ghana using machine learning
Date
2020
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Abstract
The economy of many African countries depends on the agriculture sector. However, the agriculture
sector is at the mercy of FAW following the arrival of the pest in 2016. The pest is
more dangerous to crops than other crop pests because they attack many crops, especially cereal
crops, which are the staple food in Ghana. The most widely used technique to control the
pest in the country is pheromone-baited traps and manual surveillance. Although these techniques
are cheap to implement, they are prone to errors. This paper explores how to use machine
learning techniques to detect Fall armyworm (FAW) in Ghana. The paper discusses various data
preprocessing methods such as data augmentation, transfer learning, convolutional networks for
classification as well as using object detection algorithms to detect the worm.
Description
Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020
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Undergraduate thesis
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Keywords
fall armyworm (FAW), Convolutional Neural Network (CNN), insect pest recognition, models, models