An improved Crop Disease idetentification bas...

An improved Crop Disease idetentification based on Convolution Neural Network Model

Author Name : Madan Mohan Mishra, Dr. Pramod Singh

ABSTRACT The Indian Government has fully committed to ensuring food availability for everyone, but plant diseases remain a significant challenge in achieving this goal. Plants have to contend with various types of diseases such as Abiotic, algal, bacterial, fungal, insect-transmitted, nematodes, parasite attacks, protozoa, and viral infections are always lurking from sowing to harvesting. Excellent seeds, a healthy environment, soil nutrients, timely irrigation, and proper plant care would be helpful for better crop yield. NASNetLarge, VGG19, ResNet50 and InceptionV3 models are various kinds of Convolution neural network model is a prominent technology helpful for reading the current state of the crop images. Bacterial infections are not visualized in initial state and farmers are not familiar with computers and predictions of Algorithms. Image has the collection of pixels, each digital pixel is the smallest unit of the digital image in x and y axes are also in horizontal and vertical page orientation. Plant diseases identification are too tough by naked eyes in initial state like insect, pest and bacterial attack pathogens symptom information are not visualize in primary scale.