Classification of C. annuum and C. frutescens ripening stages: how well does deep learning perform?

• Main Authors: Ibrahim, Zarina, Hanafi, Marsyita, Shafie, Siti Mariam, Syed Ahmad, Sharifah M.
• Format: Article
• Language: English
• Published: International Islamic University Malaysia-IIUM 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/113920/1/113920.pdf; http://psasir.upm.edu.my/id/eprint/113920/; https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/2769
• Institution: Universiti Putra Malaysia

Chilli is one of the world’s most widely grown crops. Among all of the chilli variants, C. annuum and C. frustescents are the most prevalent and consistently liked variants in Asia, where it is appreciated for its strong taste and pungency. Nevertheless, harvesting at the proper ripening stage according to their colour, size, and texture is essential to ensure the best quality, marketability, and shelf life. Currently, visual inspection is the primary method used by farmers, which is time-consuming and complicated. Even though automated chilli classification using computer vision and intelligent methods has received scholars’ attention, the classification of C. annuum and C. frustescents ripening stages using deep learning models has not been extensively studied. Hence, this study aims to investigate the effectiveness of three deep learning models, namely EfficientNetB0, VGG16 and ResNet50, in classifying chilli ripening stages into unripe, ripe, and overripe classes. We also introduce a huge dataset comprising 9, 022 images of C. annuum and C. frustescents chilli under various growth stages and imaging conditions which provides sufficient samples for the deep learning modelling. The experimental results show that the ResNet50 model outperforms other models with more than 95% accuracy for all classes.