Agriculture shows an important role in the enlargement of an economy. The proposed system explores how utilizing digital footprints might help improve agricultural practices and production. Agricultural data is increasingly available in digital form because of the development of modern technology and the spread of connected devices. These data can be utilized to produce digital footprints that document the whole lifecycle of agricultural production, from planting to harvesting. Farmers may then decide when to sow, water, fertilize, and harvest their crops by using machine learning algorithms trained on these digital footprints to spot patterns and forecast results. The study examines various machine learning methods that can be used to examine digital footprints and shows the advantages of doing so, including higher yields, better resource management, and lower costs. The timing of crop planting, fertilization, and harvest has long been determined by the agriculture sector using conventional techniques and experience. However, with the expansion of agricultural data available, there is now a chance to use machine learning algorithms to recognize trends and forecast outcomes based on this data. This study explores the potential of machine learning methods, particularly Random Forest optimization, to improve agricultural practices and boost output. The study uses Random Forest optimization, a potent machine learning method that can examine massive datasets, to forecast crop yield based on many variables like weather patterns, soil quality, and fertilizer application. The study's findings show how well the Random Forest optimization technique predicts crop productivity with an accuracy of 99.97%. By giving farmers useful information about forecasting crop yields, they may increase their overall productivity by using their resources wisely. The report also emphasizes the necessity of additional study and funding for the creation of machine learning algorithms that are specifically customized to the peculiarities of the agricultural sector, as well as the significance of data quality and privacy in the collecting and analysis of agricultural data. A combination of hardware and software tools, including data collection sensors, data processing equipment, graphics processing units (GPUs), cloud computing services, and mobile applications, will be needed for the hardware implementation of increasing agricultural practices in production using machine learning with Random Forest optimization techniques.
