IMPLEMENTASI DEEP LEARNING UNTUK IDENTIFIKASI UMUR TANAMAN BERDASARKAN CITRA DAUN PADA SMART FARMING - Budi Prayitno; Pritasari Palupiningsih; Farhan Muhamad Ikhsan (Jurnal Teknoinfo, 2025)
Technology plays an important role in optimizing agricultural production, one of which is through the application of smart farming. Smart Farming is a paradigm in agriculture that utilizes information and communication technology (ICT). The case study raised in this study is the use of smart farming in determining plant age. Plant age is an important factor in determining the harvest. Plants that are harvested at the right time can produce quality products in optimal quantities. Traditional farmers determine plant age manually. This has challenges, namely the process takes a long time and a lot of energy, especially for large agricultural areas. Plant age must be identified quickly and easily, the results of plant age identification are accurate and consistent and can be applied to large agricultural areas. The urgency of this research is the creation of a deep learning model that is used to detect the optimum plant age with a high accuracy value. The importance of this research lies not only in the development of technology but also in its contribution to the farmer's economy and the progress of the agricultural sector. This study aims to implement deep learning to form a classification model for identifying plant age based on leaf images and to evaluate the classification model to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preparation, modeling, and evaluation. The deep learning method used is classification with the application of the Convolutional Neural Network (CNN) VGG architecture algorithm, which has been proven effective in image analysis. The results of this study are Research on age classification models on plant leaf images using the classification method with the CNN algorithm is carried out with the stages of data collection and class division, image resizing, data augmentation, adding keras models, convolution, max pooling, flatten, relu, and with the training of 20 epochs. The results of model formation with the CNN algorithm using VGG16 get higher accuracy than VGG19. The best accuracy value is 78% from the confusion matrix results using VGG19 with a data ratio of 60% training data, 20% validation data, and 20% testing data.