A novel smartphone application for early detection of habanero disease.
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Date
2024-01-23
Journal Title
Journal ISSN
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Publisher
Scientific Report.
Abstract
Habanero plant diseases can significantly reduce crop yield and quality, making early detection and
treatment crucial for farmers. In this study, we discuss the creation of a modified VGG16 (MVGG16)
Deep Transfer Learning (DTL) model-based smartphone app for identifying habanero plant diseases.
With the help of the smartphone application, growers can quickly diagnose the health of a habanero
plant by taking a photo of one of its leaves. We trained the DTL model on a dataset of labelled images
of healthy and infected habanero plants and evaluated its performance on a separate test dataset.
The MVGG16 DTL algorithm had an accuracy, precision, f1-score, recall and AUC of 98.79%, 97.93%,
98.44%, 98.95 and 98.63%, respectively, on the testing dataset. The MVGG16 DTL model was then
integrated into a smartphone app that enables users to upload photographs, get diagnosed, and
explore a history of earlier diagnoses. We tested the software on a collection of photos of habanero
plant leaves and discovered that it was highly accurate at spotting infected plants. The smartphone
software can boost early identification and treatment of habanero plant diseases, resulting in higher
crop output and higher-quality harvests.
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Citation
Babatunde et. al., 2024