Empowering Non-Experts: A Web-Based Solution for Collaborative Image Annotation in Machine Learning Models for Computer Vision

As the global population is expected to exceed 9.7 billion by 2050, improving the efficiency of food production systems is crucial to achieving food security. This article discusses the issues of growing populations, rising food consumption, and the need for technological revolution in the agricultural sector. Leveraging robotic technology, particularly in environments like orchards and greenhouses, holds promise in enhancing production efficiency. This work proposes a solution harnessing artificial intelligence and computer vision to mitigate labeling errors in image datasets often used for supervised learning models, offering a user-friendly interface for modifying class labels. The proposed system simplifies the annotation process through a simple architecture comprising front-end, back-end, and file storage modules, ensuring accuracy and adaptability, specifically tailored for individuals with agricultural expertise, yet lacking experience in AI or Computer Science.