AI DRIVEN FARMLAND CLASSIFICATION AND SUSTAINABLE AGRICULTURE MANAGEMENT

India, with 157.35 million hectares of agricultural land, heavily relies on traditional farming practices.However, improper land utilization leads to soil degradation, inefficient crop planning, and reduced yields.According to the National Sample Survey, over 50% of Indian farmers lack access to precision farming techniques.The objective of AgriVision is to employ AI and deep learning for accurate farmland classification, optimizing agricultural land use.This project focuses on land classification using machine learning, specifically targeting the classification of various land categories such as Annual Crop, Forest, Residential, and River, among others.The background of this project is rooted in the growing need for precise land-use mapping and monitoring, which is essential for applications in agriculture, urban planning, and environmental conservation.Statistical techniques and machine learning models like Decision Tree Classifier and VGG16 are employed to classify images based on their land use.The traditional manual systems for land classification, such as visual inspection and land surveys, are time-consuming, prone to human errors, and often costly, making them inefficient for largescale projects.These methods face significant challenges in handling complex datasets with high variability in land features, leading to inaccurate classifications and delayed results.The motivation for this project arises from the need to automate land classification efficiently, ensuring faster, more accurate, and cost-effective mapping processes.This approach leverages deep learning to reduce human intervention and enhance the accuracy of classifications by identifying complex patterns in the data.The proposed system utilizes VGG16, a pre-trained deep learning model, to classify satellite images into different land categories, offering improved performance compared to traditional methods.This system aims to automate the process, reducing manual labor and human errors while delivering high-quality results.The manual system's problems, such as subjectivity, inconsistency, and limited scalability, can be mitigated with this deep learning-based approach.By automating the process, the proposed system offers an efficient solution for large-scale land classification, promoting better decision-making in sectors like agriculture, environmental monitoring, and urban development.