BUILDING A MOBILE APP FOR DETECTING STRAWBERRY DISEASES IN VIETNAM USING DEEP LEARNING

Abstract

The advancements in computer vision have opened up potential solutions
for agriculture, enhancing the quality and yield of agricultural products,
boosting economic competitiveness, and reducing labor costs. However, the
detection of diseases on fruits before harvest still relies heavily on the
experience of farmers, posing challenges in controlling and stabilizing product
quality. To address this issue, we propose a mobile application for both
Android and iOS operating systems, utilizing image processing technology
and artificial intelligence to identify diseases on strawberries. The proposed
model, YOLOv8, with minimal parameters, is applied to evaluate and detect
diseases with high accuracy. This method is considered a feasible solution to
meet the demand for disease detection on strawberries.