A Deep Learning Based Bio Fertilizer Recommendation Model Based on Chlorophyll Content for Paddy Leaves

Rice is the main agricultural product in India, with 90% of the population consuming it as a staple food. Nutrient deficiencies in rice leaves during growth period causes imbalances leading to reduce in crop yield. Growth of the paddy plants is highly related to the chlorophyll content present in it. Chlorophyll content in a leaf is a key indicator of greenness of a leaf and identifies the nutrient deficiencies in plants. Chlorophyll in plants contains nitrogen which is responsible for the photosynthesis process. In this research, chlorophyll and nitrogen contents are measured for the paddy leaves. Here, Support Vector Machine (SVM) Regression and Convolutional Neural Network (CNN) models are used to measure the chlorophyll and nitrogen content in plants. In this research, the color was the main parameter used to quantify chlorophyll and nitrogen contents in plants and RGB color model is used. Bio fertilizers is an influencing factor in yield progression and physiological processes as Bio fertilizers supply necessary nutrients to plants and enhance chlorophyll content in leaves. The chlorophyll and nitrogen concentrations were measured and then based on the measured nitrogen concentration, the appropriate bio fertilizer is recommended by the SVM classification model to enhance the nitrogen content in the plants. Here the CNN (99.98%) algorithm works better than the SVM algorithm in prediction of Chlorophyll and nitrogen contents. Hence, the Convolutional Neural Network model is built to predict the chlorophyll and nitrogen contents for the paddy leaves based on color and recommends the appropriate bio fertilizer to improve plant growth.