Fruit Ripeness Analysis and Harvest Time Optimization.

Subjective fruit harvest timing based on farmer experience and yield loss from early/late harvest.

REF: AGR-12
Agriculture, Livestock & Environment

Operational
Mechanism.

Technical details of field integration, data flow, and autonomous decision algorithms.

Edge Hardware

Hyperspectral Camera (400–1000nm)
RTK-GPS
NVIDIA Jetson Orin NX
Lighting Kit

Artificial Intelligence

Navigation (SLAM)

Under-tree autonomous navigation between fruit orchard rows

Perception & Analysis

Hyperspectral Fruit Ripeness Index (Brix, pH, Color) and Harvest Day Prediction

Autonomous Workflow

The robot roams autonomously between fruit orchard rows; scans the ripeness profile of fruits on each tree with hyperspectral camera.

Fruit sugar content (Brix), acidity (pH), and color ripeness measured non-contact; ripeness index assigned to each tree.

AI predicts optimal harvest day on a tree-by-tree basis; yield loss from early harvest prevented.

Tree-level ripeness map transmitted to harvest planning system; labor and logistics resources optimized.

Operational ROI.
Strategic Decision.

EXPECTED BALANCE IMPACT

Annual $52,000 yield and quality gain. ROI: 2.6x | Payback: 1.8 Years.

Reduces yield loss by average 18% with optimal harvest timing; raises fruit quality grade.

Review Architecture
Plans with
Professional Presentation.

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