Maize Leaf Disease Detection Using Convolution Neural Network

M. Bhanusri, N.V.K.Ramesh, Shaik Razia

There are number of cereals and grains produced in different places of the world. Inferable from changing climate conditions, crops frequently get influenced, accordingly agriculture yield diminishes radically. Harvests may get helpless against diseases brought about by parasitic, bacterial, infection, and so forth sickness causing specialists in most noticeable awful conditions. However new technologies has to be adopted to increase the crop yield. This adopted method will be effectively Identify the diseases of corn leaf through neural network. This research gives a best method to detect healthy and effected region of the leaf by using neural network for corn leaf. It is exceptionally hard for human eyes to identify the careful kind of leaf sickness of entire yield by naked eyes. To detect and classify the corn disease we need fast automatic process so we use deep learning techniques. This model is proposed to detect the diseases using neural network architecture and also provided the causes and suitable precautions of infected leaf. Major stages via image acquisition stage, pre-processing stage, segmentation of input leaf, feature extraction and classification and detection stage.

Volume 12 | Issue 2

Pages: 1154-1160

DOI: 10.5373/JARDCS/V12I2/S20201148