💧 Fertigation Intelligence Precision irrigation and fertilization decision system based on a multi-factor nonlinear regression model integrating soil, plant, and environmental data.
Nutrient Uptake (Mature Coconut, 60−80 nuts/year) Nutrient Uptake (kg/tree/yr) Function N 0.55−0.65 Leaf growth, protein synthesis P₂O₅ 0.25−0.30 Flower differentiation, fruit development K₂O 0.80−1.00 Fruit expansion, quality (most important!) MgO 0.15−0.20 Chlorophyll synthesis CaO 0.20−0.25 Shell mineralization, cell wall
Prediction Model Yield = f(N, P, K, H₂O, T, A, S) + ε R² = 0.915 — versus field yield dataRMSE = 8.3 nuts/tree/year Input factor Category Variables Soil nutrients (N/P/K/OM/pH/EC) Chemical 6 Water supply (rainfall/irrigation/ET) Physical 3 Climate (GDD/cold stress/sunshine) Environmental 3 Tree info (age/variety/CT score) Biological 4
Technical Outputs ✅ Annual fertilization schedule (rate, timing, formulation per growth stage) ✅ Irrigation regime design (drip/micro-sprinkler scheduling) ✅ Organic fertilizer substitution recommendations ✅ Intercropping fertigation coordination Field Application 11 technical guides published (2018−2025)Hainan provincial standard DB46/T 558-2021 Coconut Cultivation Technical Regulations Applied across 5,000+ acres in Wenchang, Qionghai, Wanning 