Applications of Machine Learning and Deep Learning in Smart Agriculture

Agricultural productivity can be increased using the machine learning (ML) and deep learning in a smartest way. The Food and Agriculture Organization report indicates that the productivity of the crop is getting increased. Smart agriculture is one of the best ways to check the productivity of crops. The applications of ML and deep learning identify and eliminate issues, which occur in the growth of agricultural crops. A ML technique is used in the decision making to meet specific requirements. In the modern era, smart agriculture plays a vital role in identifying the decision making and prediction over the crop yield. Due to the lack of farmers, the productivity of the crop gets decreased. To improve the productivity of the crop, the monitoring of the crop and agricultural pests had to be controlled using the smart agriculture. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making. These techniques identify the fast development in the artificial intelligence in the agricultural field. The smart farming concept monitors all the procedures, disease prediction, and agricultural pests. Deep learning explores the advantages of smart farming. To extract the useful type of information from large datasets, ML is used. These applications are used in different sectors, such as biomedical informatics, malware intrusion system, and retrieval of information. The complex structural characteristics are analyzed and other meteorological climatic factors are analyzed using deep learning. These techniques are feasible and cost‐effective in predicting the diseases in the crop. To sense the image classification of agriculture, the deep‐learning methods develop the ability of agricultural research. Precision agriculture can be able to drive crop management. ML enhances the iterative process by discovering knowledge based on patterns and their associations. These can improve the productivity of the crop worldwide as the demand for the crops is high. The algorithm aims to attain an accurate output with better prediction results. ML can improvise the vision over the image quality and classify the different yields over the crops.