A portable in-situ near-infrared LEDs-based soil Nitrogen sensor using artificial Neural Network

Abstract

Monitoring soil Nitrogen content for palm oil cultivation is paramount to produce high-quality palm oil. This study aims to investigate the feasibility of a proposed portable near-infrared (NIR) light emitting diodes (LEDs)-based soil Nitrogen sensor in predicting the soil Nitrogen content using artificial neural network (ANN). First, soil samples that collected from a local oil palm plantation were scanned using the developed sensor and then followed by a conventional method, i.e. Kjeldahl analysis to measure the actual soil Nitrogen content. ANN was used for Chemometric analysis to develop a predictive model to in-situ predict the soil Nitrogen content using the near infrared light. The performance of ANN was validated using leave one out cross-validation. Results indicate that ANN with one hundred hidden neurons achieved the best accuracy with a root mean square error of cross-validation of 0.031%. This finding suggests that the proposed portable sensor coupled with ANN is promising to satisfactorily predict soil Nitrogen content.