| dc.description.abstract | Mango (Mangifera indica) is one of the well-known and beloved fruits, as well as an
economic commodity in Indonesia. However, the cultivation process of mango plants is
not always smooth and often encounters attacks from diseases and pests that can cause
damage to the leaves, decrease fruit production, and even lead to the death of the plants.
This has led farmers to resort to using pesticides, which can be harmful to the
surrounding environment and health. Therefore, a system is needed that can utilize
color and texture images of the leaves to quickly and accurately detect diseases and
pests on mango leaves, allowing for better management and reduced usage and impact
of pesticides.In this study, the You Only Look Once version 5 (YOLOv5) method is used
to detect types of diseases and pests on mango leaves in real-time, consisting of three
classes: Mango Leaf Webber, Leaf Rolling Weevil, and Javanese Grasshopper. The
dataset used comprises 1,250 data points, with 1,000 data points for training and 250
data points for validation. For testing, 120 data points are used, collected through
smartphone cameras. The implementation of YOLOv5 method for detecting diseases
and pests on mango leaves achieved an accuracy rate of 93.3%. These results
demonstrate that the system created using the YOLOv5 method performs well in
detecting types of diseases and pests on mango leaves. | en_US |
