Research & Publications | Arpan Pramanik

[05]PEER-REVIEWED PUBLICATIONS & SCIENTIFIC RESEARCH

CONFERENCE PAPERS, IEEE/SPRINGER PUBLICATIONS & AI RESEARCH.

4 Conference Papers (IEEE & Springer)

AUTHOR SHIP & RESEARCHERS

Shibdas Dutta•Subhrendu Guha Neogi•Diya Chanda•Arpan Pramanik•Özgün Girgin•Enes Ladin Öncül

// ABSTRACT SUMMARY

This paper presents a novel multi-task deep learning framework for simultaneous fruit classification and quality assessment using a multi-headed Convolutional Neural Network (CNN). The proposed model achieves state-of-the-art performance on a curated dataset of four Indian fruits (apple, banana, guava, and orange) with two quality classes (good and bad), achieving 98% accuracy in fruit classification and 99% accuracy in quality detection.

EMPIRICAL PERFORMANCE METRICS & SPECIFICATIONS

fruit Accuracy

98%

quality Accuracy

99%

dataset

4 fruits, 2 quality classes

deployment

Streamlit interface

// MODEL ARCHITECTURE & PIPELINE

Multi-headed CNN with shared feature extractor, EfficientNetB3 architecture, Grad-CAM for interpretability

// INDEXING KEYWORDS

Fruit Quality ClassificationMulti-Task LearningCNNExplainable AIGrad-CAMAgricultural AutomationStreamlit Deployment

IEEE / Springer DOI Code Repository

CONFERENCE PAPERS, IEEE/SPRINGER PUBLICATIONS & AI RESEARCH.

FruitQ-GradeX: Determining Fruit Quality and Grading with Explainable Deep Learning

Hyperspectral Fruit and Vegetable Classification Using Convolutional Neural Networks with EfficientNetB3

Cropsense: Explainable Deep Learning Framework for Accurate Quality Detection in Solanaceous Crops

An Explainable Deep Learning Approach for Quality Assessment in Solanaceous Crops