Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.081131
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 11, 2017.
Abstract: Agriculture is the backbone to the living being that plays a vital role to country’s economy. Agriculture production is inversely affected by pest infestation and plant diseases. Plants vitality is directly affected by the pests as poor or abnormal. Automatic pest detection and classification is an essential research phenomenon, as early detection and classification of pests as they appear on the plants may lead to minimizing the loss of production. This study puts forth a comprehensive model that would facilitate the detection and classification of the pests by using Artificial Neural Network (ANN). In this approach, the image has been segmented from the fields by using enhanced K-Mean segmentation technique that identifies the pests or any object from the image. Subsequently, features will be extracted by using Discrete Cosine Transform (DCT) and classified using ANN to classify pests. The proposed approach is verified for five pests that exhibited 94% effectiveness while classifying the pests.
Muhammad Hafeez Javed, M Humair Noor, Babar Yaqoob Khan, Nazish Noor and Tayyaba Arshad, “K-means Based Automatic Pests Detection and Classification for Pesticides Spraying” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081131