Kothi Madhuri

Agriculture plays a major role in food production and economic development. Farmers and agricultural organizations often face challenges in monitoring crop conditions, identifying drought risks, and understanding how changing weather patterns affect agricultural productivity. Traditional monitoring methods mainly depend on manual inspections and delayed reporting, which makes it difficult to identify crop stress at an early stage. This project proposes the development of an AI-powered Smart Agriculture Monitoring and Crop Intelligence Platform that uses satellite vegetation data and weather information to analyze agricultural conditions. The system will analyze NDVI vegetation patterns, rainfall levels, and temperature trends to monitor crop health and identify drought-prone regions [1]. The project will use MODIS NDVI satellite datasets and NOAA weather datasets to perform exploratory data analysis, vegetation monitoring, drought analysis, and GIS-based visualizations. In addition, simple machine learning models will be implemented to classify crop conditions and identify possible drought risks.

Customer reviews have emerged as a strong source of information in the digital era, which affects brand perception and consumer decision. The presence of millions of reviews created by users on the e-commerce platforms like Amazon offers insightful information on customer preferences, products quality, and overall customer satisfaction. To prevent businesses falling behind in terms of competition and customer focus, this mass of textual data, primarily unstructured, requires a look into. The project is expected to create a machine learning-based project relying on Natural Language Processing (NLP) methods to analyze Amazon reviews and derive meaningful information about customers. The system will be used to analyze sentiments, classify the text and extract the topics in order to know about how the customers feel and trends in their responses. We can also consider advanced NLP models like word embeddings and transformer-based models which can be used to increase the accuracy of the data analysis. The projected result of this project is to have a complete analytical model that assists business to be aware about customer sentiment, product problems and improve decision-making. Using insights that are based on the data, the organizations in question can enhance their customer satisfaction as well as brand perception, along with making their marketing better.