Published Paper Details:

The monetary growth of a country mainly depends upon the agricultural conditions of that country. Meeting the food needs of the current population is becoming more difficult due to population growth, climatic fluctuations, and resource inadequacy. Smart farming, additionally referred to as precision agriculture, and has appeared as an advanced technique to tackle modern difficulties in order to provide agricultural sustainability. Precision agriculture (PA) is a cutting-edge farming method for maximizing crop yield, minimizes waste, and enhances sustainability by utilizing technology. PA increases productivity and resource efficiency by endowing farmers to make knowledgeable decisions that are customized to individual areas within their fields through the use of data from soil sensors, satellite imagery, and drones. The shift is greatly aided by machine learning (ML), which analyzes and discerns patterns in enormous amounts of agricultural data to provide useful insights. An orderly analysis on the applications of machine learning has been presented by the author in this article. Various ML algorithms like deep learning, regressions analysis, CNN have been applied to monitor the crop health and its yield prediction. It also includes the crop disease prediction at earlier stage. In order to predict outcomes like when to plant, when to water, and whether to apply pesticides, these algorithms can process inputs from a variety of sources, including climatic data, soil composition, and real-time sensor data. They do this by learning from past trends. Farmers can apply site-specific management techniques with the integration of ML, optimizing crop yield while reducing environmental impact. This article examines the limitations and challenges encountered by precision agriculture regarding management of data, technology adoption, and cost-effectiveness.