Crop Classification System in Agriculture

Abstract

Agriculture plays a crucial role in Malaysia's economy, providing employment opportunities, income, and ensuring food security. The country's diverse climate and geography contribute to a wide range of agricultural products. However, farmers face challenges in identifying crop diseases and damage, leading to time‐consuming inspections and potential human errors during large‐scale crop harvesting. In order to address these issues, this project aims to design and develop a computer vision‐based system for classifying the condition of crops. By leveraging computer vision, the system eliminates the need for manual inspection and empowers farmers with an efficient and accurate tool for assessing crop quality. Visual cues, such as colour are automatically analysed by the YOLOv8 model to detect signs of damage in each chili crop. The crop classification system was evaluated using a separate dataset consisting of 300 chili samples. The results demonstrated the effectiveness of the YOLOv8 model, achieving an impressive accuracy of 99.67%. The system exhibited perfect precision in identifying bad chilis and a recall rate of 99.33% for capturing the majority of bad chilis present in the dataset. This study signifies the potential of the YOLOv8 model as a reliable tool for predicting chili crop quality. By implementing this system, farmers can save significant time and effort previously spent on manual inspections.