Plant disease classification system using deep learning

In agriculture production, the unlimited and no disease plant product from farming become important things due this product is necessities in terms foods and mainly source for highly nutrient to community regardless country in the world. However, an increase in the human population requires an increase in agricultural production. Generally, the most important thing in agriculture that affects the quantity and quality of crops is plant diseases and make crop disease become a major threat to food security. In general, a farmer knows that his plant is attacked by a disease through direct vision. But, this process is sometimes inaccurate. Plant diseases are not only a threat to food security at the global scale, but can also have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. With the development of machine learning technology, plant disease classification can be done automatically using deep learning. In deep learning, a convolutional neural network (CNN) is a class of deep neural networks, most commonly applied to analysing visual imaginary. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels that scan the hidden layers and translation invariance characteristics