Case retrieval
CBR · kNNRetrieve the k most similar past villages; reuse their proven crop mix as a warm start.
formula
sim(q, c) = 1 / (1 + Σ wᵢ (qᵢ − cᵢ)²)Technology
The math inside the engine.
The Verdant Intelligence Platform runs a five-discipline AI stack — perception, retrieval, optimization, forecasting and coordination — that turns raw field data into demand-matched planting decisions, in real time.
- AI disciplines
- 5
- Data sources / plot
- 6
- Core models
- 3
- Solver families
- 6
The pipeline
Five stages, one decision
Each discipline is a stage in a single pipeline. Perception reads the field, retrieval recalls what worked, optimization allocates planting to demand, forecasting points it at the market, and coordination keeps every tier in lockstep. The output is one decision the platform can act on.
- 01
Perception
CVCrop, disease, yield & grading from imagery.
- 02
Retrieval
CBR · kNNReuse the proven crop mix of the most similar villages.
- 03
Optimization
MILPAllocate planting to the demand cap — never overproduce.
- 04
Forecasting
MLForecast demand on live order data.
- 05
Coordination
MASOrchestrate every tier of the supply chain.
Data & perception
Six sources, fused — then seen
Every plot is described by six fused data sources. On top of that, a computer-vision perception layer reads crop type, disease, yield and grading directly from satellite and UAV imagery — turning pixels into the ground-truth features the engine reasons over.
Climate
Soil
Agronomy
Price history
Live demand
Remote sensing
perception: crop · disease · yield · grading — from satellite & UAV
Perception layerModels & algorithms
Three models do the heavy lifting
The engine is small and legible by design: retrieve the most similar cases, allocate planting to the demand cap, and forecast where demand is heading. Here is the math, in the open.
Demand-driven allocation
MILPPlant to the demand cap — never overproduce. π price · ρ yield · κ cost · A area · D demand.
formula
max Σ (E[πⱼ]ρₚⱼ − κₚⱼ) xₚⱼ s.t. Σ xₚⱼ ≤ Aₚ, Σ ρₚⱼxₚⱼ ≤ DⱼDemand forecasting
MLForecast season-ahead demand and price from live order data and channel signals.
formula
D̂ⱼ,ₜ₊₁ = f(orders, price, season, channel)The decision loop
Decision loop
The five disciplines compose into one closed loop — featurize, retrieve, reuse, forecast, optimize, validate, deploy, then retain the outcome so the next village starts warmer. It runs end to end, every season.
Solver lineup
exact MILP (small) · GA · SA · PSO · Tabu Search (large) · learned policy
Exact MILP for small instances; metaheuristics and a learned policy scale the same objective to large ones.
verdant://engine/decision_loop
01q = featurize(village) # data layer02C = Retrieve top-k sim(q,·) # CBR03ŷ = Reuse(C) -> crop priors04D = Forecast demand # ML05x* = argmax margin(x; ŷ,D,s,A) # MILP06while not Validate(x*): Revise # feedback07deploy order-based planting(x*)08Retain(village, x*, outcome) # learnCoordination
The platform is the broker
Four agents wrap the chain — plot, aggregator, demand and quality. Each senses its node and streams state to the platform; the platform queries the engine and routes the decision back. Every link runs both ways, in real time.
农户 · 地块
Farmer / plot agent
what each plot can grow
集货 · 分级
Aggregator agent
grading, batching, logistics
The broker
Verdant Intelligence Platform
Senses every node, queries the engine, routes the decision back — two-way, real-time.
4 agents · whole-chain coordination
需求 · 买家
Demand / buyer agent
live orders & price signals
质量 · 溯源
Quality / trace agent
CV grading & traceability
From crop to cluster
How one crop becomes a whole industry
The engine doesn't stop at planting. Six fusion modes extend a single crop into a Big-Industry cluster and an eco-community — measured along six dimensions of value.
01
Internal-agri fusion
农业内部融合
02
Value-chain extension
产业链延伸
03
Function expansion
功能拓展
04
New-tech infusion
新技术渗透
05
Multi-format composite
多业态复合
06
Agri-town integration
产城融合
Six dimensions of value
GreenClusterDigitalBrandInnovationIntegration
Grounded in research
Research & perspectivesArchitecture grounded in Case-Based Reasoning & Multi-Agent Systems research for agri-food supply chains (Procedia Computer Science 232, 2024).
Research & perspectives
White Paper14 min
Demand-capped planting: an optimization view of rural overproduction
How a demand-cap constraint (MILP) reframes the smallholder planting decision and structurally removes synchronized glut.
ReadMethod9 min
Case-Based Reasoning as a moat: why every village compounds the next
Retrieve, reuse, revise, retain — and why a real case base does not copy the way software does.
ReadField Note11 min
From “one flower” to “one chain”: the Datong deployment
A first-deployment account of pairing AI coordination with on-the-ground operating capability.
Read拾千农 · 得万村 · Onboard a thousand farmers, reach ten thousand villages.
See the technology in action
VerdantSource turns coordination into compounding returns — connecting fragmented supply to real demand, village by village.