Classification of Macronutrient Deficiencies in Maize Plant using Machine Learning

Abstract

Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. Manual checking of plant nutrient deficiency does not give adequate result as naked eye observation is old method and requires more time for deficiency recognition, and also need expert person. which is very costly for farmers hence it is non-effective. To overcome disadvantages of traditional eye observing technique, we used the application of computer vision and machine learning techniques offers new prospects in nondestructive field-based analysis for nutrient deficiency detection. In the proposed system, we are using unsupervised machine learning algorithm, which learns features on its own from given dataset. Initially, we perform preprocessing on image to be tested. preprocessing includes image enhancement, image restoration, image segmentation. classifier predicts the result based on the image we passed. The system will provide the definite result about a nutrient deficiency along with disease if any and also provide solution for nutrient deficiency.