Leguminous seeds detection based on convolutional neural networks: Comparison of Faster R-CNN and YOLOv4 on a small custom dataset

This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and it can be very difficult to distinguish between them. Botanists and those who study plants, however, can identify the type of seed at a glance. As far as we know, this is the first work to consider leguminous seeds images with different backgrounds and different sizes and crowding. Machine learning is used to automatically classify and locate 11 different seed types. We chose Leguminous seeds from 11 types to be the objects of this study. Those types are of different colors, sizes, and shapes to add variety and complexity to our research. The images dataset of the leguminous seeds was manually collected, annotated, and then split randomly into three sub-datasets train, validation, and test (predictions), with a ratio of 80%, 10%, and 10% respectively. The images considered the variability between different leguminous seed types. The images were captured on five different backgrounds: white A4 paper, black pad, dark blue pad, dark green pad, and green pad. Different heights and shooting angles were considered. The crowdedness of the seeds also varied randomly between 1 and 50 seeds per image. Different combinations and arrangements between the 11 types were considered. Two different image-capturing devices were used: a SAMSUNG smartphone camera and a Canon digital camera. A total of 828 images were obtained, including 9801 seed objects (labels). The dataset contained images of different backgrounds, heights, angles, crowdedness, arrangements, and combinations. The TensorFlow framework was used to construct the Faster Region-based Convolutional Neural Network (R-CNN) model and CSPDarknet53 is used as the backbone for YOLOv4 based on DenseNet designed to connect layers in convolutional neural. Using the transfer learning method, we optimized the seed detection models. The currently dominant object detection methods, Faster R-CNN, and YOLOv4 performances were compared experimentally. The mAP (mean average precision) of the Faster R-CNN and YOLOv4 models were 84.56% and 98.52% respectively. YOLOv4 had a significant advantage in detection speed over Faster R-CNN which makes it suitable for real-time identification as well where high accuracy and low false positives are needed. The results showed that YOLOv4 had better accuracy, and detection ability, as well as faster detection speed beating Faster R-CNN by a large margin. The model can be effectively applied under a variety of backgrounds, image sizes, seed sizes, shooting angles, and shooting heights, as well as different levels of seed crowding. It constitutes an effective and efficient method for detecting different leguminous seeds in complex scenarios. This study provides a reference for further seed testing and enumeration applications.