Sensing Plant Disease Through the Utility of Deep Learning
Saravanan K1, Hareeharan E2, MohamedIrfan A3, Kalyan Kumar JS4
1Mr. Saravanan K*, Assistant Professor, Department of Information Technology, R.M.D Engineering College, Thiruvallur, Tamil Nadu, India.
2Hareeharan E,Currently Pursuing a Bachelor’s Degree in the Stream Information Technology at R.M.D Engineering College, Thiruvallur, Tamil Nadu, India.
3Mohamed Irfan A, Currently Pursuing a Bachelor’s Degree in the Stream Information Technology at R.M.D Engineering College,Thiruvallur, Tamil Nadu, India.
4Kalyan Kumar JS, Currently Pursuing a Bachelor’s Degree in the Stream Information Technology at R.M.D Engineering College, Thiruvallur, Tamil Nadu, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on June 10, 2020. | PP: 649-652 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6463069820/2020©BEIESP | DOI: 10.35940/ijitee.H6463.069820
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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: Crop diseases were one of a serious hazard to food preservation, but that the rapid identification continues tough against numerous segments regarding the globe’s way to the shortage of mandatory infrastructure. The series of stimulating global Smart phone penetration including up to date advances also latest traits paved the way for deep Learning knowledge practicing public data sets of infected crops and also healthy plant leaves gathered beneath controlled stipulations, A deep CNN to pick out various crop species including its illnesses(disease) is developed. To verify the feasibility of this method that the trained model has to reach a great efficiency on a held-out check set. Then with the help of online sources testing the model toward a collection of pictures gathered from depended. The random selection is only supported by this accuracy implies an awful lot on the pinnacle, general accuracy can be boosted by the more various sets of training records. Overall, The way of training the deep gaining knowledge of forms on increasingly huge plus publicly to be had image data-sets provides a clear pathway closer to telephone-assisted crop ailment report on a big global scale.
Keywords: Disease Detection, Deep learning, Tensor flow.
Scope of the Article: Deep Learning