This is like having a very smart weather and farming advisor that looks at years of farm data (weather, soil, fertilizer, satellite images, etc.) and predicts how much crop—especially palm oil—you will harvest before you plant or long before harvest.
Traditional yield prediction relies on expert intuition, field surveys, and late-stage measurements, which are slow, expensive, and often inaccurate. This work surveys how machine learning methods can predict crop yields (with special emphasis on palm oil) earlier and more accurately, enabling better planning of inputs, logistics, pricing, and risk management.
Domain-specific data assets (historical yields, plantation management data, localized weather and soil data) combined with tuned ML models for specific crops/regions (e.g., palm oil estates in Southeast Asia) and integration into existing agronomy and planning workflows.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Data availability and quality (consistent yield, weather, soil, and management practice records); model generalization across regions and climate conditions.
Early Majority
Compared with generic crop-yield ML work, this focuses specifically on palm oil, likely surveying which algorithms and input features work best for that crop’s phenology and management practices, giving palm-oil producers and solution vendors a more targeted blueprint than broad agriculture ML surveys.