Early Detection and Categorization of Corn Leaf Diseases using Deep Learning Model

Author Details

1. Jayanthi¹, A.Ahamed Noorea Fayaz², S.Poovarasan³, A.Praveen⁴

¹Professor and Head, ^2,3,4UG Students – Final Year, Department of Electronics and Communication Engineering, Nandha College of Technology, Perundurai, Tamilnadu, India

Abstract

Agriculture is a very significant field for increasing population over the world to meet the basic needs of food. Many farmers are cultivating in remote areas of the world with the lack of accurate knowledge in disease detection, however, they rely on manual observation on grains and vegetables, as a result, they are suffering from a great loss. Accurate detection of corn leaf diseases is a complex challenge faced by farmers during the growth and production stages of corn. Digital farming practices can be an interesting solution for easily and quickly detecting plant diseases. To address such issues, this paper proposes a method based on an improved deep learning Convolutional Neural Network (CNN) model for accurately detecting three common diseases of corn leaves: gray spot, leaf spot and rust. First, this technique is applied on Kaggle datasets of corn leaves to investigate the symptoms of unhealthy leaf. Then, the feature extraction and classification process are performed in dataset images to detect leaf diseases using CNN model with applying image processing. For three corn leaf diseases, the approach citied in this paper has an average accuracy of 96%. It has a better accuracy than the other pretrained model. The deep learning algorithm proposed in this paper is of great significance in intelligent agriculture, ecological protection and agricultural production.

Keywords:  Cornleafdiseasedetection,Deeplearning,Convolutional Neural Network(CNN), Image processing.