An Empirical Survey of Machine Learning Based Plant Disease Prediction Models

The occurrence of diseases in plants badly impacts the agricultural production, which increases food insecurity when are left undetected. Particularly important for ensuring availability production and major crops, such as maize, rice, others. Effective control prevention based on disease forecasting early warning, is essential managing making decisions regarding productivity. In rural parts developing nations, observations by knowledgeable providers remain main method plant identification yet. This draws researchers ongoing experienced monitoring, may be cost-prohibitive large farms. Besides, some remote areas, farmers require assistance experts, expensive time-consuming process. Hence, automatic to promote monitoring crop fields, encourages contribution accurate, less-expensive, automatic, fast technique perform detection plants. this survey, methods used deep learning discussed. importance demonstrated through schematic sketch other basic machine techniques applications.