Apple Quality Detection with YOLO
Automated fruit grading and defect identification using lightweight YOLO models running natively on Android devices.
Project Overview
The Apple Quality Detection with YOLO project involved developing an Android app tailored for apple orchards and agricultural sorting facilities. Using custom-trained YOLO object detection models, the app classifies apples into grade levels (Premium, Good, Defective/Bruised) directly in the field, helping farmers streamline post-harvest grading and boost harvest yield valuation.
Objective
The objective was to provide growers with an immediate, objective grading tool:
Automated Apple Quality Assessment
To develop an app that can automatically classify apples based on quality, providing instantaneous grading feedback.
Defect and Bruise Identification
To identify subtle surface blemishes, pest damage, and bruising that diminish market value.
In-Field Offline Usability
To allow orchard workers to grade fruit directly on trees or collection bins without requiring network connectivity.
Approach
To meet these objectives, sevvenlabs followed these key strategies:
YOLO Model Integration
Trained a specialized YOLO architecture on a comprehensive dataset of labeled fruit varieties across multiple maturation stages.
Model Optimization for Mobile
Applied 8-bit post-training quantization (PTQ) to shrink model footprint below 15MB with minimal precision degradation.
Seamless Android Integration
Built a clean Kotlin Android app integrating CameraX for effortless point-and-scan camera analysis.
Problems & Solutions
Challenges overcome during model training and field deployment:
Real-Time Classification on Mobile Devices
Real-time object detection models often demand high GPU capacity, which is absent on field-level Android hardware.
Engineered lightweight input pipelines with dynamic frame downscaling, enabling continuous 30 FPS inference on standard Android chipsets.
Ensuring Accurate Apple Classification
Natural variations in fruit color, shadows under orchard leaves, and surface reflections often triggered false bruise detections.
Built an extensive illumination-invariant training set with multi-spectral color normalization to ensure robust blemish recognition in direct sunlight.
Results
The deployment delivered measurable enhancements to the harvesting pipeline:
1. Increased Agricultural Efficiency
Sorting throughput increased by 40% compared to traditional visual manual inspection.
2. High Defect Detection Precision
Achieved 96.2% accuracy in identifying surface blemishes and bruise categorizations.
3. Reduced Post-Harvest Waste
Enabled farmers to segregate lower-grade fruit for immediate processing rather than shipping spoiled produce.
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