Detail publikace

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.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

3. 7. 2023

Nakladatel

PERGAMON-ELSEVIER SCIENCE LTD

Místo

OXFORD

ISSN

0045-7906

Periodikum

COMPUTERS & ELECTRICAL ENGINEERING

Ročník

109,Part A

Číslo

July 2023

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

14

Strany počet

14

URL

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

Plný text v Digitální knihovně

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"
}