CNN algorithm for plant classification in deep learning

Abstract The prior methodology of characterizing the plants for dependent on surface based order and another strategy depends on KNN classifier. This paper presents qualities examination of plants utilizing picture preparing methods for robotized vision framework utilized at horticultural field. In farming examination, the programmed plant attributes recognition is fundamental one in observing huge field. The proposed dynamic framework uses picture content portrayal and regulated classifier sort of neural organization. This will naturally distinguish the plant species when we import its picture as info. Picture preparing strategies for this sort of choice investigation includes pre-processing and characterization stage. At Processing, an info picture will be resized and commotion expulsion procedure is applied. At definite stage the neural organization orders the pictures as farming plant, harmful plant and therapeutic plant separately. At that point it will show the attributes of each plant.

[1]  Dong Liang,et al.  New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  M. Ramakrishnan,et al.  Groundnut leaf disease detection and classification by using back probagation algorithm , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[3]  Leisa Armstrong,et al.  A survey of image processing techniques for agriculture , 2014 .

[4]  K. Thangadurai,et al.  Computer Visionimage Enhancement for Plant Leaves Disease Detection , 2014, 2014 World Congress on Computing and Communication Technologies.

[5]  Konstantinos P. Ferentinos,et al.  Deep learning models for plant disease detection and diagnosis , 2018, Comput. Electron. Agric..

[6]  Monika Jhuria,et al.  Image processing for smart farming: Detection of disease and fruit grading , 2013, 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013).