Applications of Deep Learning and Machine Learning in Smart Agriculture

Abstract

Machine learning (ML) and deep learning can be used in the smartest way possible to improve productivity in agriculture. The Food and Agriculture Organization's research shows that the crop's production is rising. One of the finest methods to monitor agricultural yield is through smart agriculture. Applications of ML and deep learning help to discover and resolve problems that crop growth encounters. In agriculture, the production of crops can be enhanced by applying machine learning and deep learning methodologies. These methods demonstrate the rapid advancement of artificial intelligence in the agriculture sector. The idea of “smart farming” keeps an eye on all processes, disease prediction, and agricultural pests. ML is used to extract meaningful information from huge datasets. Deep learning evaluates structural characteristics, meteorological data, and climatic factors to help anticipate agricultural diseases using practical and economical techniques. The deep-learning techniques enhance agricultural research's capacity to sense the overall classification of agriculture.