INFLUENCE OF LEAF IMAGING DISTANCE ON WATER GUAVA CLASSIFICATION USING NEURAL NETWORK WITH GRAY LEVEL CO-OCCURRENCE MATRIX FEATURES - Muhammad Haviz Irfani; Gasim; Andika Afrianto (Jurnal Riset Informatika, 2025)
The development of Computer Vision technology has made a significant contribution to the agricultural sector, particularly in the identification of plants based on visual characteristics. Water guava (Syzygium aqueum) is one of the fruit commodities widely cultivated in Indonesia; however, its seedling varieties are often difficult to distinguish visually. Conventional methods relying on human observation tend to have low accuracy, highlighting the need for an accurate and efficient identification system from the early stages. This study aims to analyze the effect of varying imaging distances on the extraction results of leaf vein texture features using the Gray Level Co-occurrence Matrix (GLCM) method and to evaluate how this parameter influences the classification performance of water guava seedlings using the Backpropagation Artificial Neural Network (ANN). Unlike previous GLCM–ANN plant classification studies that primarily focused on lighting or species variation, this work systematically investigates imaging distance as a key factor in optimizing texture feature stability and improving model accuracy. Experiments were conducted using five imaging distances—7 cm, 9 cm, 11 cm, 13 cm, and 15 cm—with 2,500 images used for training data and 500 images for testing data. The results show that an imaging distance of 13 cm yielded the best performance, achieving 80% accuracy, where 80 out of 100 test images were correctly classified, supported by balanced precision, recall, and F1-score values indicating stable and reliable classification performance.