Modern Approaches to Image Segmentation in Agriculture

Abstract

Modern agriculture faces challenges in crop health monitoring, yield prediction, environmental sustainability,
and resource management, necessitating advanced computational solutions. Traditional agricultural techniques
often rely on manual inspection and subjective decision-making, leading to inefficiencies and lower productivity.
This paper presents FarmaVision, an AI-powered agricultural analysis system integrating computer vision and
machine learning techniques to enhance crop health assessment, disease identification, soil health monitoring,
fertilizer recommendation, and environmental impact analysis. The system utilizes Convolutional Neural
Networks (CNNs) for plant disease identification, achieving 97.77% accuracy, image segmentation for soil
analysis, Random Forest for crop yield prediction with 97% accuracy, and AI-driven fertilizer recommendations
with 99% accuracy. Additionally, the system incorporates real-time environmental impact monitoring, assessing
soil health, water usage, and carbon footprint reduction to promote sustainable farming practices. The integration
of deep learning, image processing, and real-time analytics demonstrates significant improvements in agricultural
decision-making, optimizing productivity and sustainability