Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

Abstract

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.