I’d set up three overlapping dashboards: the Growth‑Predictor, the Drop‑Probability and the Weather‑Risk. For Growth‑Predictor I’d log fruit size, fruit weight, chlorophyll index, leaf nutrient levels and the local light exposure. Drop‑Probability would use those values plus a moving‑average of fruit‑to‑leaf ratio, a moisture‑stress factor, and a calculated “sag index” from repeated weight‑lifting tests. Weather‑Risk tracks temperature, humidity, wind gusts, soil moisture and any pest‑activity alerts. Every data point feeds into a Bayesian tree that updates the 48‑hour drop‑chance, so you can see the probability curve shift in real time.

Got it—I'll add a “bird‑activity” layer to the Drop‑Probability dashboard and a “rustle‑intensity” sensor to catch micro‑turbulence. The data will still drive the model, but I’ll treat the birds and leaf rustle as external priors to keep the system from over‑confidence.