Published Paper Details:

One's agriculture is fundamental in providing food security and fostering securities across economies in the world. Many of today's primary agricultural issues such as climate change, soil depletion, water scarcity, pest control, and the pressing requirement for sustainable agriculture are some of the stated issues that cannot be resolved using conventional techniques Agriculture has been a cornerstone of food production and economic stability throughout the world. There has been a transformative change in technology over the past few years Machine Learning (ML), which is a subset of Artificial Intelligence (AI), considers various prediction models to predict outcomes on record data associated with the agricultural field. The corresponding algorithms can now identify and forecast various phenomena that have limitless potential in the agricultural sector. One of them is the forecasting of crop yields and also the early-stage identification of plant diseases and pest infestation, while monitoring soil conditions, optimizing the fuel and fertilizer, automating the elimination of weeds, and advancing achieved precision agriculture. This paper looks at the impacts of machine learning as it pertains to agriculture, analyzes existing use case machine learning implementations, discusses challenges to widespread adoption, and presents areas needing further research. The illustrations provided reaffirm the fact that machine learning stands to greatly benefit the agricultural sector in increasing food production, maintaining environmental balance, improving resources utilization efficiency, and assisting farmers in meeting future demands.