Enhanced Convolution Neural Network for Tomato Leaf Disease Classification
C. P. Saranya1, D. Palanivel Rajan2, M. Mythili3, K. Pushpalatha4, V. Saranya5

1C.P.Saranya, Assistant Professor, Department of CSE, Coimbatore Institute of Engineering and Technology ,Coimbatore,Tamil Nadu, India.
2Dr.D.PalanivelRajan, Professor, Department of CSE, C M R Engineering College, Hyderabad, Telangana, India.
3M.Mythili, Assistant Professor, Department of Information Science and Engineering, Vemana Institute of Technology, Bengaluru, Karnataka, India.
4Dr.K.Pushpalatha, Associate Professor, Department of IT, Coimbatore Institute of Engineering and Technology ,Coimbatore, Tamil Nadu, India.
5V.Saranya. Assistant Professor, Department of CSE, Tamil Nadu College of Engineering, Coimbatore, Tamil Nadu, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3973-3976 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8970038620/2020©BEIESP | DOI: 10.35940/ijrte.F8970.038620

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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: When plants and crops are affected by pests it affects the agricultural production of the country. Agricultural productivity depends heavily on the economy. This is one of the reasons why plant disease detection plays a major role in agriculture. Usually farmers or experts observe the plants with naked eye for detection and identification of disease. But this method can be time processing, expensive and inaccurate. Detection of crop disease using a few instantaneous strategy is helpful as it decreases comprehensive surveillance job in huge crop farms and locates disease side effects quite soon, i.e. if they tend on leaves and stems. Enhanced Convolutional neural networks (ECNN) have demonstrated great performance in object recognition and image classification problems. Using a public dataset images of infected and healthy Tomato leaves collected under controlled conditions, we trained a deep convolutional neural network to identify diseases in tomato. As the result, few diseases that usually occur in tomato plants such as Late blight, Gray spot and bacterial canker are detected.
Keywords: Deep learning, Enhanced Convolutional Neural Network (ECNN), Leaf Prediction Tool (LPT), Tomato Leafs, Deep learning.
Scope of the Article: Deep Learning.