Publication detail

Expert system for smart farming for diagnosis of sugarcane diseases using machine learning

ATHEESWARAN, A. K. V., R. CHAGANTI, B. N. L. MARAM, A. HERENCSÁR, N.

Original Title

Expert system for smart farming for diagnosis of sugarcane diseases using machine learning

Type

journal article in Web of Science

Language

English

Original Abstract

Agriculture is one of the oldest occupations in the world and continues to exist today. In some form or another, the world's population depends on agriculture for its needs. The major loss in sugarcane production in India is due to pests, plant disease, malnutrition, and nutrient deficiency in plants. To identify these diseases, farmers go to local farmers, experts, agricultural people, and fellow neighbors to identify the problem caused. In some cases, their information may be adequate, but in others it is not. These people cannot solve all the problems caused by their crops can be solved by these people; there is a need to accurately predict the correct disease and provide the proper treatment at the right time. This can only be done by applying machine learning-based Internet of Things solutions in real time. This article proposes a method for a smart farming system to address the needs of farmers producing sugarcane in India by applying intelligent solutions that use image processing and soft computing. Four sugarcane diseases are investigated, such as Eyespot, Leaf Scald, Yellow Leaf, and Pokkah Boeng, and three characteristics such as color, shape, and texture. Images were used for training data in Artificial Neural Network (ANN), Neuro-Fuzzy, and Case-Based Reasoning (CBR) algorithms, and the performance of the feature extraction technique was evaluated in terms of sensitivity, specificity, F1 score, and accuracy.

Keywords

ANN; CBR; Feature extraction; Fuzzy logic; Median filtering; Neuro-fuzzy; Smart farming

Authors

ATHEESWARAN, A.; K. V., R.; CHAGANTI, B. N. L.; MARAM, A.; HERENCSÁR, N.

Released

3. 7. 2023

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

Location

OXFORD

ISBN

0045-7906

Periodical

COMPUTERS & ELECTRICAL ENGINEERING

Year of study

109,Part A

Number

July 2023

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

14

Pages count

14

URL

https://www.sciencedirect.com/science/article/pii/S0045790623001635

Full text in the Digital Library

http://hdl.handle.net/11012/209424

BibTex

@article{BUT183454,
  author="Athiraja {Atheeswaran} and Raghavender {K. V.} and B. N. Lakshmi {Chaganti} and Ashok {Maram} and Norbert {Herencsár}",
  title="Expert system for smart farming for diagnosis of sugarcane diseases using machine learning",
  journal="COMPUTERS & ELECTRICAL ENGINEERING",
  year="2023",
  volume="109,Part A",
  number="July 2023",
  pages="1--14",
  doi="10.1016/j.compeleceng.2023.108739",
  issn="0045-7906",
  url="https://www.sciencedirect.com/science/article/pii/S0045790623001635"
}