Plant Disease Detection using CNN Techniques

1S Sivakama Sundari, 2 Dr. Sampath AK, 3Dr. M. Islabudeen

1 M. Tech Artificial Intelligence, Department of CSE, School of Engineering, Presidency University, Bangalore

2 Associate Professor CSE, School of Engineering, Presidency University, Bangalore

3 Associate Professor CSE, School of Engineering, Presidency University, Bangalore

1 sivakama.20212AIE0007@presidencyuniversity.in, shivagama02@gmail.com, 2sampath.ak@presidencyuniversity.in, 3 islabudeen@presidencyuniversity.in

Abstract: Plants are the meals supply of the earth. Plant infections and illnesses are consequently a first-rate threat, however the maximum not un-usual place prognosis is basically to study flora for the presence or absence of visible symptoms. The agricultural production of the country gets affected majorly due to pests as they affect the plants and crops. The detection and identification of disease is been observed by farmers and experts through their naked eyes. Based on the leaf image classification, an approach of plant disease recognition model is being developed with the help of deep convolutional networks. Early detection of diseases to which plants are exposed is very important, especially in a country like India with a large population. The diseases caused by bacteria, virus and fungus results on lowering the crop yield in a huge aspect. The loss can be prevented by predicting the plant disease at the earliest. With the help of Deep Learning concepts, the performance and accuracy of disease detection can be improved. It uses image processing concepts for noise reduction, ML and DL concepts i.e., CNN for Problem Solving. This project captures plants and leaf disease and helps farmers to identify and detect the solutions for the problem that is being infected.

Keywords: Plant Disease Recognition Model, Image Classification, Plant Disease Recognition, Image Processing, Machine Learning Concepts, Deep Learning Concepts