Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

• Main Authors: Arie Qur'ania, Prihastuti Harsani, Triastinurmiatiningsih Triastinurmiatiningsih, Lili Ayu Wulandhari, Alexander Agung Santoso Gunawan
• Format: Article
• Language: English
• Published: Bina Nusantara University 2020-05-01
• Series: CommIT Journal
• Subjects: color extraction; edge detection; nutrient deficiencies; artificial neural networks
• Online Access: https://journal.binus.ac.id/index.php/commit/article/view/5952

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and P and P and K. The researchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.