Research Article
Design of Intelligent Insect Monitoring System Using Deep Learning Techniques
@INPROCEEDINGS{10.4108/eai.7-6-2021.2308788, author={V.Ceronmani Sharmila and Neeraj Chauhan and Rajdeep Kumar and Suraj Kumar Barwal}, title={Design of Intelligent Insect Monitoring System Using Deep Learning Techniques}, proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India}, publisher={EAI}, proceedings_a={I3CAC}, year={2021}, month={6}, keywords={insect or pest classification algorithm insect or pest detection algorithm machine learning cnn algorithm k-means clustering algorithm}, doi={10.4108/eai.7-6-2021.2308788} }- V.Ceronmani Sharmila
Neeraj Chauhan
Rajdeep Kumar
Suraj Kumar Barwal
Year: 2021
Design of Intelligent Insect Monitoring System Using Deep Learning Techniques
I3CAC
EAI
DOI: 10.4108/eai.7-6-2021.2308788
Abstract
The agriculture field is growing up its potential to better the demand of food and delivers healthy and nutritious foodstuff. This project presents an insect or pest detection and classification for the plant using machine learning. It’s a challenging part for the farmers to the crops which are acquiring defective and the degree or grade of excellence is also getting reduced day by day due to various pest or insect attacks. Earlier insect identification has been a big issue due to not well-skilled taxonomists to name the insects based on surface structure construction features accurately. Here, the proposed system will be a research tool for the study of early insects on the plants and leaves which will classify it using CNN and K-Means Clustering algorithm in machine learning. The detection analysis of insects was executed with shorter computational point in time for wang dataset using insect image Median Filter. The final classification results of accuracy were utilized to identify the pest and insects in the earliest period and increased the period to grow the harvest fertility and crop degree of excellence in the field of agriculture.