TY - JOUR
T1 - Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System
AU - Nagasubramanian, Gayathri
AU - Sakthivel, Rakesh Kumar
AU - Patan, Rizwan
AU - Sankayya, Muthuramalingam
AU - Daneshmand, Mahmoud
AU - Gandomi, Amir H.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2021/8/15
Y1 - 2021/8/15
N2 - Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K -nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.
AB - Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K -nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.
KW - Agriculture
KW - Internet of Things (IoT)
KW - crop and leaf diseases
KW - ensemble support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85104259023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104259023&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2021.3072908
DO - 10.1109/JIOT.2021.3072908
M3 - Article
AN - SCOPUS:85104259023
VL - 8
SP - 12847
EP - 12854
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 16
M1 - 9403896
ER -