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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
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" }