A specific fine-grained identification model for plasma-treated rice growth using multiscale shortcut convolutional neural network

As an agricultural innovation, low-temperature plasma technology is an environmentally friendly green technology that increases crop quality and productivity. However, there is a lack of research on the identification of plasma-treated rice growth. Although traditional convolutional neural networks (CNN) can automatically share convolution kernels and extract features, the outputs are only suitable for entry-level categorization. Indeed, shortcuts from the bottom layers to fully connected layers can be established feasibly in order to utilize spatial and local information from the bottom layers, which contain small distinctions necessary for fine-grain identification. In this work, 5000 original images which contain the basic growth information of rice (including plasma treated rice and the control rice) at the tillering stage were collected. An efficient multiscale shortcut CNN (MSCNN) model utilizing key information and cross-layer features was proposed. The results show that MSCNN outperforms the mainstream models in terms of accuracy, recall, precision and F1 score with 92.64%, 90.87%, 92.88% and 92.69%, respectively. Finally, the ablation experiment, comparing the average precision of MSCNN with and without shortcuts, revealed that the MSCNN with three shortcuts achieved the best performance with the highest precision.