Real-Time Classification of Chilli Ripeness using Convolutional Neural Network (CNN)

Abstract

Chilli harvesting plays an important role in Malaysia’s economy as it is one of the crops with high demand in the country. Normally, farmers harvest and categorise the ripeness of chillies by using the naked eye which can lead to errors and human fatigue. To overcome the limitations of this manual harvesting, an automated real-time chilli vision system that can classify between ripe and unripe chillies were developed. This research involved with a diverse dataset of chilli images using various chilli varieties and growth stages. The YOLOv8 model was trained using Google Colab's GPU-accelerated environment to optimize the performance. The model's deployment for real-time inference and classification was facilitated through Visual Studio Code, with HSV colour analysis used to differentiate between ripe and unripe chillies. CNN was used to validate and analyse the accuracy of the proposed system. As a result, the system achieved an accuracy of 88% for chilli classification. These findings proved the potential of Artificial Intelligence (AI)-driven systems in supporting precision agriculture.