Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o