Project Paper Name - “A Comparative Study Of Different CNN Architectures for Cotton Disease Detection Using Transfer Learning”

ABSTRACT :

Agriculture is the most important sector in many countries like India. Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly these constraints are identified as diseases and pests that are hard to detect with bare eyes. While keeping track of diseases with plants with the help of specialists is very costly in agricultural field. Manually detecting all the crops in a large field is also very difficult task. There would be a system which can identify the diseases of plants automatically, will bring revolution in monitoring large crop fields and then plant leaves can be taken cure as soon as possible after detection of disease. This study focused to develop a Convolutional Neural Network (CNN) model to boost the detection of cotton leaf disease using the deep learning technique. For using this model user needs to upload the image into the application and the Artificial Intelligence (AI) can detect the diseases from the Cotton-leaf using image processing technique. In this paper, at first we are going to build different types of CNN (Convolutional Neural Network) model by using Transfer Learning techniques with different CNN architectures or pre-trained models like MobileNet ResNet50, EfficientNetB0, InceptionV3 etc. The CNN model, which will provide better accuracy in detection of diseases of the cotton leaves, that model we will go to implement after comparative studying and analyzing these CNN (Convolutional Neural Network) architecture.

Key Words: Agriculture, Cotton, Image Processing, CNN, MobileNet, ResNet50, InceptionV3, Transfer Learning.

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