Agro Product System

Abstract

This project investigates the application of machine learning algorithms to predict agricultural product yield. The objective is to develop a predictive model that accurately forecasts yield based on various environmental and agronomic factors. The scope includes data collection, preprocessing, model training, and evaluation. The conclusion demonstrates that machine learning significantly improves yield prediction accuracy compared to traditional methods, enabling more efficient resource allocation and improved farm management.

Introduction

Global food security is increasingly challenged by fluctuating weather patterns, pest infestations, and inefficient resource management. Accurate prediction of agricultural product yields is crucial for mitigating these challenges. Existing methods often rely on historical averages and expert judgment, lacking the precision and adaptability required for modern agriculture. This project addresses this gap by leveraging the power of machine learning to build a robust and accurate yield prediction model.

Objectives

• Develop a machine learning model to accurately predict agricultural product yield.
• Compare the performance of different machine learning algorithms for yield prediction.
• Deploy the best-performing model for practical application in farm management.