Nitrogen Deficiency Detection in Paddy for Urea fertilizer Management

EasyChair Preprint 5437, version 1

Abstract

Plants need nutrients to survive and thrive. It is essential that they receive the right type and quantity of nutrients at the right time. Nitrogen is the major nutrient involved in growth of paddy crops and is the most common nutrient in commercial fertilizers. These fertilizers are added based on predefined values which are based on land size, age of the crop, and is usually recommended by the fertilizer seller. This approach is not sustainable as it does not account for the soil fertility level. This results in mismanagement and over fertilization which has detrimental effects to the environment. Soil fertility, and understanding the various nutrient levels is key in proper plant growth, as too much of a particular kind can have detrimental effects on plant growth, by preventing uptake of other essential nutrients. Lack of nutrients is also problematic as it can stunt plant growth. Currently soil testing by sending samples to labs is the well-established practice, which is time consuming and generally not followed for every growing season. Paddy is one of the staple cereal crops in India, and largely consumed in south India. It is nutrient demanding crop, which need good levels of major nutrients, especially nitrogen for high yield.  In this paper we review the recent technologies used to estimate and identify nitrogen deficiency levels in paddy crops. Computer models are capable of classifying plants as healthy and not healthy, and identify nitrogen deficiencies, using image processing, machine learning, and deep learning techniques.

Keyphrases: Artificial Intelligence, deep learning, image processing, machine learning

@booklet{EasyChair:5437,
  author    = {P Jagadish and M Merlyn Mercylona and M Aishwarya and Namrata Karki},
  title     = {Nitrogen Deficiency Detection in Paddy for Urea fertilizer Management},
  howpublished = {EasyChair Preprint 5437},
  year      = {EasyChair, 2021}}