Revolutionizing agriculture: unveiling yolov-8- powered multi-labled seeds segmentation

Abstract

There is a wide range of dry seeds in the world. The quality of seeds significantly impacts crop production. Hence, seed classification is vital for both marketing and production, serving as the foundation for sustainable principles in agriculture systems. The primary objective of this study is a method for extracting multiple seeds from a single image by segmenting them. Thus, a multi-label segmentation and detection system was developed to distinguish 18 different varieties of seeds with similar features. For the training model, images of 100 seeds of 18 varieties were taken with a high-resolution camera. In the initial stage, images undergo augmentation and feature extraction processes. Resulting a total of 1029 by 18 shapes of labels. The initial phase was focused on processing data and extracting the valuable features from it. For segmentation and detection, the model YOLOv8 was applied. Also, to compare the performance of our custom train YOLOv8 model we trained a vanilla YOLOv5. Our breakthrough YOLOv8 model achieved an impressive 99% confidence level for select classes in our private dataset, with an overall average detection rate of 94.5%. Whereas the YOLOv5 conducted an overall detection rate of 94.7%.