Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that difficult ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a non-destructive method nutrient monitoring control, included in the hydroponic system. vision plays role processing image data based on specific characteristics. However, analysis one characteristic cannot represent health. In addition, knowing percentage is also needed support precision agriculture systems. Therefore, we propose Multi Layer Perceptron architecture can perform multi-tasks, namely, identification estimation. optimal will be sought combination three features form texture, color, leaf shape. Based design, our proposed model high potential identifying estimating deficiency at same time as well applied Indonesia.