AI Agri Supply Chain Management: Harvest Forecast to Retail
We build turnkey AI systems for agricultural supply chain management. Agri-food chains differ from industrial logistics in three key ways. Produce spoils quickly. Volumes are seasonal and depend on weather. Regulations require end-to-end traceability at every stage. Our team covers all three: yield forecasting 2–4 months ahead, AI freshness grading at intake, cold chain optimization with FEFO dispatch, and full farm-to-fork traceability. Write to us and we'll assess your supply chain scope within 2 days. Delivery takes 12–20 weeks depending on complexity.
Our team has delivered AI supply chain systems for grain holdings, fruit and vegetable distributors, and food processors. Each project starts with a data audit: historical yield records, IoT temperature logs, and quality inspection data. We then build models on your actual data rather than generic benchmarks. The result is a system that fits your workflow and connects to your existing ERP or WMS.
Every delivery includes: yield forecasting module with satellite and agrometeo inputs, computer vision freshness classifier trained on your product categories, cold chain FEFO dispatch logic, traceability QR or DataMatrix integration, and three months of post-launch support. Our systems reduce spoilage by 15–30% within the first season of operation.
AI Crop Yield Forecasting for Supply Planning
Early forecasting runs 2–4 months before harvest. A grain holding or food processor needs to know how much raw material will arrive to plan production capacity and export contracts. We combine satellite NDVI indices for key growing regions with agrometeorological models that track accumulated temperature and rainfall totals. We calibrate models on historical yield records from your own fields and regional statistics agencies.
Short-term forecasting runs 2–3 weeks before harvest. A mobile app for agronomists captures a photo of a grain ear. A regression model based on EfficientNet predicts per-field yield from grain maturity features. Typical RMSE is ±0.4 t/ha. This gives logistics teams a precise intake forecast for scheduling storage capacity and transport routes.
Computer Vision Freshness Grading
At intake, every incoming batch is graded visually by camera. Manual inspection is slow and inconsistent between shifts. Our computer vision model classifies produce into four grades: premium, standard, near-expiry, and defective. It also estimates residual shelf life in days. This drives automated lot routing: premium goes to retail, near-expiry goes to express distribution or processing, defective is rejected at the gate.
import torch
import torchvision.transforms as T
from PIL import Image
class FreshnessPredictor:
"""Оценка свежести плодоовощной продукции по фото"""
FRESHNESS_CLASSES = {
0: 'fresh_premium', # 1-я категория
1: 'fresh_standard', # 2-я категория
2: 'near_expiry', # требует срочной реализации
3: 'defective' # выбраковка
}
def __init__(self, model_path):
self.model = torch.load(model_path, map_location='cpu')
self.model.eval()
self.transform = T.Compose([
T.Resize(224), T.CenterCrop(224),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
def predict(self, image_path):
img = Image.open(image_path).convert('RGB')
x = self.transform(img).unsqueeze(0)
with torch.no_grad():
logits = self.model(x)
probs = torch.softmax(logits, dim=1)[0]
predicted_class = probs.argmax().item()
return {
'grade': self.FRESHNESS_CLASSES[predicted_class],
'confidence': probs[predicted_class].item(),
'estimated_shelf_life_days': [14, 7, 2, 0][predicted_class]
}
For NIR spectroscopy at intake gates, portable spectrometers measure sugar, starch, and moisture content without destructive testing. Calibration models use PLS-R regression. RMSECV for sugar content stays below 0.3%. Incoming lots are split into categories at the gate with no laboratory turnaround time required.
Cold Chain Optimization and FEFO Logic
The cold chain runs from field to shelf. IoT sensors on pallets record the real temperature at every point in the chain. A machine learning model predicts residual shelf life by applying a kinetic spoilage model — the Q10 rule — with ML corrections for variety and initial condition. FIFO dispatch is replaced by FEFO, First Expired First Out, based on actual measured freshness rather than receipt date alone.
If predicted spoilage exceeds 15% of a batch, the system alerts the logistics manager with three options. The first is to accelerate delivery with priority routing. The second is to reroute the batch to the nearest processing facility. The third is partial sale at a reduced price. This decision logic is automated and reduces manual decision time from hours to seconds.
The Q10 temperature rule states that a 10-degree rise in temperature doubles the rate of biochemical spoilage reactions. Combined with real-time IoT temperature data from each pallet, this gives the model the inputs it needs to estimate residual shelf life at any point in the chain.
The model is calibrated per variety and per initial quality grade to account for product-specific spoilage kinetics. This means accuracy is highest for the crops you actually handle — not population averages from unrelated datasets.
Farm-to-Fork Traceability
Full traceability is required under GlobalGAP, EU Regulation 178/2002, and similar frameworks. Each unit carries a QR or DataMatrix code. Scanning it reveals the complete chain history: field of origin, harvest date, storage temperatures, processing stage, and retail destination.
IoT data is attached at each stage automatically. Our team integrates blockchain as an optional layer for immutable B2B audit trails.
Recall management is automated. When an unsafe batch is identified, the system builds a propagation tree in seconds. It shows which downstream lots used this material, which retail locations currently hold it, and who needs to be notified. A recall checklist with contacts and instructions is generated automatically. This reduces recall response time from days to under an hour.