Design and Development of Soil Nutrients Level Detection System based on Soil Color and pH for Crop Recommendations using Fuzzy Algorithms

Abstract

In recent years, modern farmers usually taking a soil sample to the laboratory or using a soil test kit to know soil macronutrients, i.e., nitrogen (N), phosphorus (P), and potassium (K), and pH to determine what kind of crop plant is suitable for their agriculture land. However, these manual methods are costly and time consumed. The characteristic of soil samples also possibly changing by time or contact during transport. This paper presents the design and development of a portable integrated soil macronutrient level and pH detection system that can analyze soil samples quickly. To give crop recommendations, IoT components and cloud-based fuzzy inference systems are used. The fuzzy algorithm decides the crop recommendation from the soil pH and level content of N, P, and K. The user can receive the crop recommendation via the android application. Data is sent from the portable system to the cloud system and vice versa using the internet network with HTTP request protocol. The accuracy test results of system plant decision on agricultural land were compared with the fuzzy logic method have a quite uniform crop output with a small error rate of 1,66%.