Deep Learning Model for Plant Disease Detection
D.Raghunath Kumar Babu1, M.Chaithanya2, M.Sandhya3, G.Shireesha4

1D.Raghunath Kumar Babu, Department Of CSE, JNTUACEP Badvel, India.
2M.Chaithanya, Department Of CSE,JNTUACEP, Madanapalle, India.
3M.Sandhya, Department Of CSE,JNTUACEP, Nellore, India.
4G.Shireesha, Department Of CSE,JNTUACEP, Jammalamadugu, India.

Manuscript received on April 02, 2020. | Revised Manuscript received on April 15, 2020. | Manuscript published on May 30, 2020. | PP: 750-754 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1232059120/2020©BEIESP | DOI: 10.35940/ijrte.A1232.059120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Food is one of the basic needs of human being. We know that the population is rising it is more important to feed such a huge population. But nowadays plants are largely affected with various types of diseases. If proper care should not be taken then it will show effect on quality of food products, quantity and finally on productivity of crops.. so, Early detection of plant disease is very essential, but it is very hard to farmers to monitor the crops manually it takes more processing time, huge amount of work, expensive and need expertised persons. Automatic detection of plant diseases helps the farmers to monitor the large fields easily, because our approach of using convolution neural networks provides a chance to discover diseases at the very early stage. By using Image Processing and machine learning models we can detect the plant diseases automatically but the accuracy is very less, early detection is also a major challenge. With the modern advanced developments in deep learning, in our project we have implemented the convolution neural networks(CNN) which comprises of different layers,by using those layers we can automatically detect and classify the diseases present in the plants. High Classification accuracy and more processing speed are the main advantages of our approach. After training the model on color, grayscale and segmented datasets our deep learning model will be capable of classifying a large number of different diseases and our project gives us the name of the disease that the plant has with its confidence level and also provides remedies for corresponding diseases. 
Keywords: Deep Learning, Convolution Neural Network, Accuracy, Confidence, Image Processing.
Scope of the Article: Deep Learning