Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment

Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment

Mohammad Farukh Hashmi (NIT Warangal, India), Aashish Kumar (Anurag Group of Institutions, India) and Avinash G. Keskar (VNIT Nagpur, India)
Copyright: © 2020 |Pages: 26
DOI: 10.4018/978-1-5225-9175-7.ch006


In olden days, the plants used to tolerate and minimize the effect of air pollution caused by the then established industries and some automobiles. But in today's scenario, the rate at which plants and industries are rising doesn't match the count of trees. The plant survival and metabolism are based upon the nitrogen and chlorophyll available. There are several expensive methods to determine the chlorophyll and nitrogen content of the leaf like SPAD meter; the researchers have proposed a simple, inexpensive method that precisely determines the chlorophyll and nitrogen vales with a simple input RGB image. This chapter investigates the variation of content of plants in polluted environments and pollution-free environments.
Chapter Preview

In recent past, air pollutants, responsible for vegetation grievance and crop yield losses, are causing increased concern. Urban air pollution is a serious problem in both developing and developed countries. The increasing number of industries and automobile vehicles are continuously adding toxic gases and other substances to the environment. Environmental stress, such as air pollution, is among the factors most limiting plan productivity and survivorship. Air pollution can straight away affect plants via leaves or indirectly through soil acidification. When exposed to aerial pollutants, most plants experienced physiological changes before exhibiting visible damage to leaves. The atmospheric SO2 adversely affects various morphological and physiological characteristics of plants. High soil moisture and high relative humidity aggravated SO2 injury in plants. Industrialization and the automobiles are responsible for maximum amount of air pollutants and the crop plants are very sensitive to gaseous and particulate pollutions and these can be used as indicators of air pollution. In urban environments, trees play an important role in improving air quality by taking up gases and particles. Vegetation is an effective indicator of the overall impact of air pollution and the effect observed is a time-averaged result that is more reliable than the one obtained from direct determination of the pollutant in air over a short period. Although, many trees and shrubs have been identified and used as dust filters to check the rising urban dust pollution level.

Key Terms in this Chapter

RGB Image: A bitmap image holding RGB color values in three image channels.

Deep Learning: Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised, or unsupervised.

Convolution: In mathematics (in particular, functional analysis) convolution is a mathematical operation on two functions (f and g) to produce a third function that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it.

Pooling: A pooling layer is another building block of a CNN. Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling.

Artificial Neural Networks: Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.

Machine Intelligence (AI): Artificial intelligence is branch of computer science; the machine behaves in a way as human thinks and considered as an intelligent system.

Particulate Pollution: Particulate pollution is pollution of an environment that consists of particles suspended in some medium. There are three primary forms: atmospheric particulate matter, marine debris, and space debris.

Classification: Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood.

Digital Image Processing: Digital image processing is the use of computer algorithms to perform image processing on digital images.

Computer Vision: As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data. This type of processing typically needs input data provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot.

Convolutional Neural Networks: A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vision that includes image and video recognition, along with recommender systems and natural language processing (NLP).

Complete Chapter List

Search this Book: