To achieve automatic grading of potting seedlings, improve performance, and reduce complexity, we collected and constructed a grading dataset using tomato potting seedlings as the test subject. We selected the YOLOv5n target detection model as the baseline model. We reduced the number of model parameters through lightweight improvements, integrated the similarity-based attention mechanism into the backbone network, enhanced the accuracy of potting seedling feature recognition by improving the CIOU loss function to EIOU, and performed global channel pruning on the improved model to further reduce its complexity. Experiments demonstrate the following results: the final model achieves a recall of 92.1%, an average precision mean of 94.9%, has 0.9×106M parameters, performs 2.1G floating-point operations, has a model weight size of 2.2M, and achieves a detection speed of 130 frames per second. Deployment and testing on edge devices confirm that the model achieves low computational requirements, has a small parameter count, maintains fast and accurate performance, and can be used for real-time classification of potting seedlings.
Mushrooms, as a delicacy in people's lives, are deeply loved by people, and the nutrients in mushrooms play an essential role in people's health. However, the characteristics of poisonous mushrooms and non-toxic mushrooms are extremely similar, and they are easily confused in the field of miscellaneous circumstances, and therefore often cause the eaters to ingest poisoning. The identification of poisonous mushrooms is a basic measure to avoid poisoning. At present, the methods for identifying poisonous mushrooms mainly include shape recognition method based on folk experience, chemical analysis methods, and animal testing methods. However, these methods have some disadvantages such as low accuracy in the practical application identification, complex experimental equipment required, unsatisfactory detection of unknown toxins, and long experimental period. Aim at the deficiency of the traditional poisonous mushroom identification method; this paper proposes a poison mushroom identification method based on BP neural network. Through the learning of the characteristics of the known poisonous mushroom, identify unknown poisonous mushrooms.
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