Senet Cnn Based Tomato Leaf Disease Detection
Pankhuri Pragya1, Varsha Sharma2, Vivek Sharma3

1Pankhuri Pragy, Final Year student, Master of Technology RGPV University Bhopal Madhya Pradesh, India. 
2Varsha Sharma, Assistant professor at School of Information Technology Bhopal Madhya Pradesh, India.
3Vivek Sharma, assistant professor at School of Information Technology, RGPV University, Bhopal. Madhya Pradesh, India.

Manuscript received on 29 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 773-777 | Volume-8 Issue-11, September 2019. | Retrieval Number: K14520981119/2019©BEIESP | DOI: 10.35940/ijitee.K1452.0981119
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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: Plants play important roles in the environment. Various plants are fulfilling many demands and basic requirements of the society. Saving such entities in the society is the uttermost necessity in today’s world to deal with plant degradations. Many diseases in plants are common and thus they face degradation. While mainly dealing with common plant such as tomato and potato plants, it is observed that they very often face bacterial and other diseases. A proper precaution can be made to save plant from such diseases. Thus the early prediction of such different diseases can be made, which can be a massive savings to the farmer as well as for the country economy. This paper has adapted a moderate different approach of convolutional neural network called SENet. In this approach, a hybrid process is discussed which uses the advantage of SENet and CNN layer concept for better classification. CNN is performed by using the number of layer and kernel selections. Classification of data is performed using the traditional CNN approach. In this scenario, the quick process occurrence is performed using suppression of less used information. It tries to add weight to each and every feature map in the layer. This approach is used to check, identify and detect the defects in leaf of tomato. The prime motive of the presented approach is; to obtain simple easiest method for the detection of disease in tomato leaf with use of minimal computing resources. Thus an improved, efficient algorithm can be made use in real time implementation of leaf disease prediction with high accuracy and efficiency parameters.
Keywords: Artificial Neural Networks, Convolution Neural Network (CNN), Image Processing, Leaf disease detection, SENet, Support Vector Machine
Scope of the Article: