Klasifikasi Daun Kelor Kering Berbasis Vision Artificial Intelegence

Abstract

The drying process of moringa leaves (Moringa oleifera) is necessary to reduce the moisture content so that the leaves become dry and can be utilized for the next processes. Drying moringa leaves to change the moisture content from 80% to 9.2% requires an ideal heating condition, as the heating rate must not damage the nutritional content present in the leaves. The utilization of ANN models can recognize seasonal time series data patterns. The introduction is categorized into several classifications. By using IoT, it is hoped that the drying conditions can be monitored. The system is also connected to a recommendation system using Recurrent Neural Networks (RNN), which will provide recommendations for the best conditions for moringa flour production. The Google Cloud Vision AI system suite combines artificial intelligence with other technologies to understand and analyze videos and easily integrate vision detection features into applications. These tools are available through APIs and can still be customized for specific needs. The Google Cloud Vision AI system suite combines artificial intelligence with other technologies to understand and analyze videos, as well as to easily integrate vision detection features into applications. These features include image labelling, face and structure detection, optical character recognition (OCR), and tagging of vulgar content. The test results found that the Vision AI system used has been tested to detect and classify moringa leaves, both in wet and dry conditions. The testing was conducted using a mobile-based Vision AI application and the Google Cloud Vision API. The results indicate that the system detects moringa leaves more as a plant.