A Methodology for Automatic Detection and Classification of Pests using Optimized SVM in Greenhouse Crops
Venkataramana Attada1, Somesh Katta2
1Venkataramana Attada, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India.
2Somesh Katta, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1485-1491 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8133088619/2019©BEIESP | DOI: 10.35940/ijeat.F8133.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital revolution is taking place in every industry. The technologies namely Cloud Computing and the Internet of Things (IoT) are considered to be as a digital revolution. Comparatively with other industries Agriculture industry has less usage of these digital revolutionary technologies. In recent years Agriculture industry uses such type of digital revolution technologies to counterpart traditional practices which greatly influence the productivity. The IoT is set to push the future of farming to the next level by collecting the production data which includes weather and soil data, image data of crop, pests, etc. through internet enabled communication objects. Performing computation and providing advisory on this large scale of data that is collected by communication objects by Cloud Computing technology in terms of Leaf is point of interest which has infestation problem with biological organisms such as pests observed by naked eye is time consuming. We make use of digital revolution device like Unmanned Aerial Vehicle (UAV) which collects the data from user point of inter-est, Digital Image Processing techniques, Pattern recognition Algorithms for above stated problem to develop an advisory based cloud system which provides advisory based on detection of pests present on off-seasonal crops rose, lengthy type crops cucumber which are cultivated in new agricultural farming i.e. limited space structure namely Greenhouse.
Keywords: UAV, Greenhouse, Digital image processing, Pattern recognition, Off-seasonal crops.