Application of Machine Learning and Internet of Things for Identification of Nutrient Deficiencies in Oil Palm

Abstract

Several products derived from oil palm trees are sold commercially, bringing in money for the country and the people that live there. Because of this, the land available for oil palm seed plantations will grow, which will help maintain a steady supply of high-quality oil despite the expanding population. Also, rapid increases in oil palm tree planting, especially when cultivation is out of control, lead to degradation. Because of soil erosion, soil nutrients may be lost as a result of the degradation. The growth of an oil palm tree, as well as the quality of its yields, could be stunted by a deficiency in the macronutrients (N, Mg P, K). A decrease in yield may arise from using the tried-and-true method of detecting macronutrients; this is because this method is prone to error. The current system has only provided limited dataset information and a sluggish classification performance because of its limited features. The Internet of Things (IoT) enables efficient and seamless use of data regarding oil palm tree development and fertilizer control. The environmental elements affecting the growth of young oil palm trees include temperature, nutrients, humidity, light, and soil moisture content; the conceptual framework includes deep learning, IoT technologies, machine learning and image processing. As a result, it is recommended that machine learning, the Internet of Things (IoT), and deep learning be studied for detecting the nutritional deficiencies of oil palm trees. � 2022 IEEE.