Design and Development of Electronic System for Predicting Nutrient Deficiency in Plants

Abstract

Plants require adequate nutrient content for a total as well as natural life cycle. Six macronutrients, such as nitrogen, calcium, phosphorus, potassium, sulfur, and magnesium are essential for the natural and healthy rise of plants. Regular activities with a lack of nutrients in plants lead to transportation difficulties and ultimately affect crop. Plants show a definite lack of nutrient on their leaves with notable differences in pattern. Our research suggested is to provide an automated and economically viable method for detecting defects nutritional conditions. Our system uses helpful information to forecast performance of crops. The dataset for deficient leaves and healthy leaves develop with the help of the RGB Color Extraction Analysis Technique, Disclosure of texture in real time, Identification of bottom edge, etc. This dataset will allow supervised machine learning to predict and identify accurate shortages of vitamins and healthy plants to prohibit growth rates.