Object detection model for mango leaf diseases / Muhammad Norzakwan Mohd Sham and Mohammad Hafiz Ismail - Mohd Sham, Muhammad Norzakwan; Ismail, Mohammad Hafiz (College of Computing, Informatics and Media, UiTM Perlis, 2023)
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. A deep learning method was used to develop a leaf disease object detection model. However, this project will focus on collecting datasets mango leaf disease images samples from UiTM Harumanis mango tree farm. In addition, this object detection model for mango leaf diseases used the techniques mean average precision (mAP) to performance accuracy and speed of the algorithm. This project would detect mango tree growers' leaf diseases using the YOLOv4 darknet. This model can also be utilised by homeowners that grow mango trees. On object detection, farmers can detect leaf diseases like black sooty molds and white wax scales earlier and treat them. Thus, leaf disease-detecting projects will use this feature to help the users facing this leaf disease problem. This object detection model will also benefit mango farmers and agriculture students. This study will also help farmers monitor several mango trees rapidly.