Apple | Agri-Excellence

Objectives

Development of X-Ray imaging platform for detecting internal defects in apples.
Development of a machine vision solution for the detection of Apple’s surface defects utilizing 360-degree views.
Development of an AI-based fusion model using X-ray and Visible images for the detection of internal and surface defects in apples and a sorting system.

AI-enabled conveyorized Non-destructive solution for detection of internal and external defects of apples.
AI-enabled conveyorized grading and sorting system for apples.
Integration of X-Ray and Visible Imaging, sorting machine.

Markedly reduces manual labor and human error, addressing the limitations of existing CCD-based systems that lack full 360-degree or internal detection, thus increasing the accuracy and consistency of defect sorting.
Supports Jammu & Kashmir’s target apple production expansion by providing scalable, industrial-grade sorting for both new and traditional apple varieties.
Optimizes post-harvest quality and reduces losses from invisible or latent defects, directly boosting commercial viability and market value for producers and exporters.
Model validation employs accuracy, precision, and recall metrics, with iterative hyperparameter tuning to achieve optimal performance in deep learning-based defect detection.

Project Team