Comparative Analysis of Machine Learning Models for Crop Yield Prediction Across Multiple Crop Types

In this study, we delve into the predictive capabilities of machine learning (ML) regression models across various crops, aiming to revolutionize crop yield forecasting through technological advancement. Our research meticulously assesses the performance of several prominent ML algorithms, including...

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Veröffentlicht in:	SN computer science 2025-01, Vol.6 (1), p.64, Article 64
Hauptverfasser:	Patil, Yashraj, Ramachandran, Harikrishnan, Sundararajan, Sridhevi, Srideviponmalar, P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adaptability Agricultural commodities Agricultural production Agriculture Algorithms Chickpeas Comparative analysis Computer Imaging Computer Science Computer Systems Organization and Communication Networks Corn Crop diseases Crop yield Crops Data Structures and Information Theory Datasets Decision making Decision trees Effectiveness Feature selection Gaussian process Human error Information Systems and Communication Service Machine learning Market planning Moisture content Original Research Pattern Recognition and Graphics Plant diseases Predictions Regression models Remote sensing Satellites Software Engineering/Programming and Operating Systems Sorghum Sugarcane Unleashing the Advances in ICT for Digital Transformation through Data Engineering and Data Analytics Unmanned aerial vehicles Vision Wheat
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