Abstract:
Improving yield and maintaining crop strength with optimization in use of resources are the major requirements
in smart farming. To build a smart decision support system for improving production with flexibility, it requires Remote
Sensing Systems. Now days with effective use of machine learning and deep learning techniques, it is possible to make the
system flexible and cost effective. The deep learning based system has enormous potential, so that it can process a large
number of input data and it can also control nonlinear functions. Here it should be discussed that from continuous
monitoring of crop leaves images shall ensures the diseases identification. The research concludes that the quick advances
in deep learning methodology will provide gainful and complete classification of crop with 98.7% to 99.9% accuracy. In this
research, different crop diseases are classified based on image processing and Convolutional neural network method. For
classification of maize crop diseases, different models have been developed, compared, and finally best one is found out.
Also the finest model has been tested for different crop diseases to check its consistency.
