KebunQ

KebunQ is a mobile application designed to assist home gardeners by leveraging image recognition technology to identify plant diseases in common crops such as grape, tomato, and cucumber. As gardening becomes increasingly popular, many enthusiasts struggle with diagnosing plant health issues, leading to crop failures and discouragement.

This Bangkit 2024 Capstone Project provides a solution that allows users to upload images of affected plants, enabling accurate disease identification and treatment recommendations, along with educational content on plant care.

As the Machine Learning engineer, I developed a convolutional neural network (CNN) model using TensorFlow and Keras to classify plant diseases effectively, leveraging transfer learning by fine-tuning a pre-trained MobileNet model. The model was optimized for performance and deployed to serve predictions.

The project also involved Mobile Development for the Android application with Firebase authentication, and Cloud Computing to implement REST API for seamless data communication and cloud infrastructure setup.