Android-Based Areca Plant Disease Detection Using Convolutional Neural Network (Cnn) Algorithm - Khairunnisa; Riza, Ferdy (Bahasa Indonesia, 2024)
Indonesia is included in a country that can be said to be the largest areca nut exporting country in the world with a presentation of 80%. The quality of crops is very influential on the agricultural sector and can affect the value of exports, so to maintain the quality of crops, an area nut disease detection application is designed. The purpose of this research is to present information quickly and accurately in problem solving to help detect diseases that exist in areca plants by building a Machine Learning model on the Android system using the Convolutional NEURAL Network (CNN) algorithm. Data is collected through the results of field studies and analysis of related documents to strengthen research data, so that test data is obtained as many as 10 types of diseases and 32 symptoms, image data is processed using a teachable machine. So that the test results obtained the average accuracy of the model in detecting diseases in areca nut plants is 98.7%.