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All Journal Media Informatika
Muhammad Fikri Haikal
Faculty of Computer Science, Universitas Jember
Computer Science & IT

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Systematic Literature Review : Machine Learning and Deep Learning Aproaches for Crop Yield Prediction Using Remote Sensing Data Muhammad Fikri Haikal; Muhammad Ariful Furqon
Media Informatika Vol 25 No 2 (2026)
Publisher : P3M STMIK LIKMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37595/mediainfo.v25i2.496

Abstract

Accurate crop yield prediction is essential for improving agricultural productivity and food security. This study presents a Systematic Literature Review (SLR) on machine learning and deep learning approaches for crop yield prediction using remote sensing data. Following PRISMA guidelines, 20 high-quality studies published between 2015 and 2025 were analyzed. The results highlight the growing dominance of deep and hybrid models, particularly when multi-source remote sensing data and phenological information are integrated.
Co-Authors Furqon, Muhammad Ariful