Deep Learning based Model for Plant Disease Detection
S. V. Kogilavani1, S. Malliga2
1S.V.Kogilavani*, Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.
2S.Malliga, Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on 29 September, 2019. | Manuscript published on October 10, 2019. | PP: 3416-3420 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25851081219/2019©BEIESP | DOI: 10.35940/ijitee.L2585.1081219
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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: Plant disease prediction is vital in Agriculture sector. Farmer’s economic growth depends on the quality of the products that they produce, which relies on the plant’s growth and the yield they get. Identifying the disease can lead to quicker interventions that can be implemented to reduce the effects of major economic loss. There may be high degree of complexity in diagnosing the various types of diseases in plants through leaves of the plants. Manual mode of plant disease detection is a tedious process. In this proposed work, Convolutional Neural Network methodologies like Sequential model and SmallerVGG model were utilized for detecting diseases in plants and diagnosis using plant leaf image. Among these two models, SmallerVGG model achieved more accuracy rate of 87% than 65% of sequential model.
Keywords: Convolutional Neural Network, Deep learning, Plant Disease Detection, Image Processing
Scope of the Article: Deep learning