An Integrated Deep Learning Framework of Tomato Leaf Disease Detection
Jiten Khurana1, Anurag Sharma2, Harshit Singh Chhabra3, Rahul Nijhawan4

1Jiten Khurana, Graphic Era University, Dehradun (Uttarakhand), India.

2Anurag Sharma, Graphic Era University, Dehradun (Uttarakhand), India.

3Harshit Singh Chhabra, Graphic Era University, Dehradun (Uttarakhand), India.

4Rahul Nijhawan, Graphic Era University, Dehradun (Uttarakhand), India.

Manuscript received on 08 September 2019 | Revised Manuscript received on 17 September 2019 | Manuscript Published on 11 October 2019 | PP: 46-50 | Volume-8 Issue-11S September 2019 | Retrieval Number: K101009811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1010.09811S19

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Abstract: Plants are a very important part of human life. They have variety of use as food, medication, raw materials and maintaining a balanced ecosystem. Plant disease is a deterioration of normal state of plant that interrupts and modifies its functionality. Pathogens are the main cause of such diseases. For agricultural purposes, a variety of methods have been proposed to detect plant diseases in the recent technological era. However, detecting plant diseases with high accuracy is still a challenge in computer vision. In this study, we propose an integrated deep learning framework where a pre-trained VGG-19 model is used for feature extraction and stacking ensemble model is used to detect and classify leaf diseases from images so as to reduce production and economic loses in agriculture sector. A dataset consisting of two classes (Infected and Healthy) and a total of 3242 images was used to test the system. Our proposed work has been compared with other contemporary algorithms (kNN, SVM, RF and Tree) and have outperformed by obtaining an accuracy of 98.6%.

Keywords: Disease Classification, Deep Learning Technique, Hybrid, Bacterial Spots, Leaf Curl.
Scope of the Article: Deep Learning