A novel smartphone application for early detection of habanero disease.

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.

Plant phenotyping relevance

葉画像から感染状態を推定する深層学習モデルとスマートフォンアプリの開発・評価が研究の中心であり、植物病害状態の表現型計測に該当する。

Code and data availability

The authors trained and evaluated their MVGG16 habanero disease-detection model on a public Kaggle image dataset (PlantVillage), explicitly linked in the Data availability statement. No author analysis code, trained model, or app source is publicly deposited.