Ensemble Classification and IoT Based Pattern Recognition for Crop Disease Monitoring System

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 decisionmaking 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 techniques such as support vector machine and convolutional neural network. 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 Support Vector Machine (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), naove Bayes and K-nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.