CLASSIFICATION OF DISEASE IN TOBACCO LEAVES USING DEEP NEURAL NETWORK

The quality of crop production decreases as a result of agricultural leaf diseases. The identification of leaf diseases can also be automated to increase agricultural yields. Most devices, however, are impaired of lack by different leaf disease trends that impair the detection accuracy. The computer vision framework is developed in this paper by framing a model consisting of the collection, extraction and classification of pictures. For classification of real time images, a deep learning classification, namely the Deep Neural Network (DNN) is used. The experimental findings on tobacco plants suggest that the approach suggested has increased the classification rate compared with other current approaches. The results of the classification prove whether or not the leaf is sick.