Classification of Nutrient Deficiencies Based on Leaf Image in Hydroponic Lettuce using MobileNet Architecture

Abstract

Currently the industrial sector in Indonesia is growing rapidly which shifts agricultural land to narrow. This resulted in farmers needing to look for other land to continue to be able to produce their food. Hydroponics is a farming technique using water media that utilizes narrow land. One of the plants that is often used is lettuce. However, with the application of this hydroponic technique, the quality of lettuce plants is still not good due to lack of attention to maintenance, resulting in a lack of nutrition in lettuce plants. Therefore, this research will create a nutritional deficiency classification system in hydroponic lettuce through leaf images using a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture. The results in this research test scenario obtained an accuracy of 88%. That way, it is hoped that it can help farmers to find out nutritional deficiencies in lettuce plants so that they can maintain the quality of lettuce production.