Published Paper Details:

Through the use of machine learning algorithms, automation in farming and agriculture enhances agricultural operations while advancing productivity and sustainability. This research looks at how various agricultural industries use methods of unsupervised and supervised learning. Models for estimating agricultural production are created using methods such as linear regression, which can help with data-driven choice making and allocate resources optimization. Plant diseases, pests, and deficits are automatically identified using artificial intelligence and deep machine learning techniques, allowing for timely intervention. Climatic information as well as soil analysis are used by clustering algorithms for recommending the best crops and farming practices. The goal of the project is to advance smart and environmentally conscious farming techniques by examining how machine learning may expedite agricultural operations, maximize resource consumption, and decrease adverse environmental effects.