AI Based on Inception v3 Architecture for Fault Diagnosis in paddy crop using image recognition and Transfer Learning

Plant diseases is a serious effect that greatly affects crop yield due to these diseases. The optimal diagnosis of this plant disease will prevent us from the loss in crop yield. In analyzing automatic diagnosis of the plant, disease by implementing a smartphone application established. Artificial intelligence is used is machine learning framework to detect the plant disease in time. The basic preventive measure taken using machine learning algorithms that works on an example model in which the module integrated to predict the plant diseases. The proposed structure uses a tuned Inception v3 architecture, which is a typical deep convolutional neural network with multiple convolution layers, consisting of nearly 6 convolution layers. This architecture, which use for classifying the plant disease, supports nearly 12 layers whose performance is an outstanding one for image network. The result shows that this model built using this Framework works very well in timely diagnosis of the plant diseases. Places like the learning rate position f- measure recall and disease recognition with accuracy performances investigated. Total accuracy attained using the built model is 76.56 % with maximum area of the curve.