Open	I am Prathima B A pursuing my 2nd year B Tech from School of Computer Science Engineering RV University my interests include reading novels,books

Poster Abstract

Agriculture is the backbone of human civilization, providing food, raw materials, and employment to a significant portion of the global population. The productivity and sustainability of agriculture are influenced by several key factors, including the levels of nitrogen (N), phosphorus (P), and potassium (K) in the soil, as well as environmental conditions such as temperature, humidity, rainfall, and soil pH. These factors play a critical role in determining crop health and yield. However, traditional systems struggle to effectively process and analyze the vast amounts of data generated from these variables.

The problem statement focuses on the need for accurate crop recommendations based on the analysis of crucial agricultural factors. Farmers often lack precise guidance on which crops to plant under specific soil and weather conditions. This inadequacy results in suboptimal crop selection, leading to lower yields, inefficient resource use, and potential negative impacts on the environment. There is a pressing need for an advanced system that can analyze these variables and provide reliable crop recommendations.

Given the complexity and volume of agricultural data, advanced big data systems like HPCC Systems are essential for addressing these challenges. This project uses HPCC Systems, a comprehensive big data platform, to provide accurate crop recommendations based on soil nutrient levels and environmental conditions. The project outlines the importance of each factor in agriculture, formulates a problem statement focused on the need for improved crop recommendations, and highlights the specific features of HPCC Systems utilized. These features include:

1. Random Forest Algorithm from Learning Trees: This machine learning technique is used to analyze the data and generate predictive models for crop recommendations. The random forest algorithm helps in identifying the most suitable crops based on the given soil and environmental conditions by considering multiple decision trees and aggregating their results.

2. Graph Analysis: The use of graphs in HPCC Systems allows for the visualization of relationships and dependencies between different agricultural factors. This feature helps in understanding complex interactions and patterns in the data, facilitating more accurate and insightful crop recommendations.

3. High-Performance Data Storage and Processing: HPCC Systems provide robust data storage capabilities and efficient processing power, enabling the handling of large-scale datasets. This ensures that all relevant data is considered in the analysis, leading to comprehensive and reliable crop recommendations.

By employing these features of HPCC Systems, this project aims to provide accurate and actionable crop recommendations, thereby optimizing crop yields. The integration of advanced data analysis techniques not only enhances decision-making for farmers but also contributes to sustainable agricultural practices by ensuring optimal use of resources. This approach promises to improve food security, support economic growth in agricultural communities, and promote environmental sustainability.

Presentation

In this Video Recording, Prathima provides a tour and explanation of her poster content.

Crop Recommendation:

Click on the poster for a larger image.

Open