Detection and classification of nutrient deficiencies in plants using machine learning

Abstract

Abstract Agriculture is the major factor contributing to Indian Economy. According to the current statistics, its contribution to GDP sector is 17.9%. Technical advancement in agricultural domain will produce more agricultural products without any wastage of money, time and manpower. Nutrients play a major role in plant growth. Lack of nutrients leads to reduced crop yield and plant growth. In this work, we are trying to create an artificial neural network model to recognize and classify the nutrient deficiency in tomato by examining the leaf characteristics. This will help farmers to adjust the nutrient supply to the plant. If soil lacks a specific nutrient, it will reflect in the physical characteristics of a leaf. The color and shape of a leaf are the two major features used for identifying the nutrient deficiency. The comparison of different segmentation schemes like hue based and threshold based schemes shows their influence in the performance of the proposed system. The influence of different activation functions in the artificial neural network is also studied in this work. The results show that the proposed method was able to classify and identify nutritional deficiencies with high accuracy.