|dc.description.abstract||Cassava production has been greatly affected by whiteflies, which are a key pest
and vector. When left unchecked, whiteflies can reduce crop yields, degrade cassava
quality, and, in extreme situations, result in total crop loss. As a result, knowing
the number of whiteflies is crucial for detecting, identifying, and preventing their
Visual examination and manual counting of whiteflies are the current methods for
identifying and counting whiteflies; nevertheless, these traditional ways of counting
whiteflies have drawbacks such as inefficiency and time consumption.
In the last decade, there have been significant advancements in the science of
machine learning, particularly in the field of computer vision for application such as
self-driving cars, video analytics, edge computing and better triangulation of sensor
data. In this project, we employ current advancements in computer vision to assist
in the detection and counting of whiteflies in order to help farmers in Sub-Saharan
Africa reduce food shortages and increase food security.
Research has been done by various people on the topic of detecting and count-
ing whiteflies. Faster R-CNN and Haar-Cascade, image processing techniques with
MATLAB and YOLOv3 have all been employed as approaches in detecting and
counting of whiteflies.
In this project we used the YOLOv5 which is an object detection algorithm that
came out in 2020 to tackle the same problem. During training we managed to achieve
an mAP of 0.842 @0.5IOU. The results show a 20% improvement when compared to
the current state-of-art models. The approach proposed in this project utilized tiling
operation on the image data to enhance the performance of the YOLOv5 model on
the whitefly dataset.||en_US