Year: 2025
Type: article
Abstract: This study explores the application of the Random Forest algorithm to forecast tomato yields based on climatic, soil, and crop management variables.With global food demand expected to rise by 70% by 2050, precision agriculture techniques, such as machine learning-based yield prediction, are crucial for enhancing resource allocation and improving productivity.The research engages a dataset comprising weather conditions, soil properties, and agronomic practices to train and authenticate a Random Forest model.Key findings indicate that temperature (32%) and rainfall (25%) are the most significant yield determinants, while the model achieves an R² score of 0.95, indicating high predictive accuracy.The study enumerates the potential of Random Forest in empowering data-driven farming decisions, reducing waste, and enhancing food security.Recommendations include the implementation of predictive analytics by farmers especially the large-scale ones, policy interventions for climate-resilient agriculture (which can gain prominence with the right push in Nigeria), and future research integrating deep learning techniques for improved accuracy.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4414254428
FWCI:
Cites:
Cited by:
Related to: