Sunflower seeds classification based on sparse convolutional neural networks in multi-objective scene

Generally, sunflower seeds are classified by machine vision-based methods in production, which include using photoelectric sensors to identify light-sensitive signals through traditional algorithms for which the equipment cost is relatively high and using neural network image recognition methods to identify images through cameras for which the computational cost is high. To address these problems, a multi-objective sunflower seed classification method based on sparse convolutional neural networks is proposed. Sunflower seeds were obtained from the video recorded using the YOLOv5 Object detection algorithm, and a ResNet-based classification model was used to classify the seeds according to differences in appearance. The ResNet has the disadvantages of having numerous parameters and high storage requirements; therefore, this study referred to the Lottery Ticket Hypothesis and used the Iterative Magnitude Pruning algorithm to compress the sunflower seed classification model, aiming to ascertain the optimal sparse sub-network from the classification model. Experiments were conducted to compare the effects on model performance before and after pruning, pruning degree, and different pruning methods. The results showed that the performance of the ResNet-based sunflower seed classification model using global pruning was the least affected by pruning, with a 92% reduction in the number of parameters, the best accuracy is 0.56% better than non-pruned and 9.17% better than layer-wise pruning. These findings demonstrate that using the Iterative Magnitude Pruning algorithm can render the sunflower seed classification model lightweight with less performance loss. The reduction in computational resources through model compression reduces the cost of sunflower seed classification, making it more applicable to practical production, and this model can be used as a cost-effective alternative to key sunflower seed classification techniques in practical production.