Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.
We present the results of the reinforcement of plant root systems in surface soil in a model test to simulate actual precipitation conditions. In the test, Eleusine indica was selected as herbage to reinforce the soil. Based on the various moisture contents of plant roots in a pull-out test, a fitting formula describing the interfacial friction strength between the roots and soil and soil moisture content was obtained to explain the amount of slippage of the side slope during the process of rainfall. The experimental results showed that the root systems of plants successfully reinforced soil and stabilized the water content in the surface soil of a slope and that the occurrence time of landslides was delayed significantly in the grass-planting slope model. After the simulated rainfall started, the reinforcement effect of the plant roots changed. As the rainfall increased, the interfacial friction between the roots and the soil exhibited a negative power function relationship with the water content. These conclusions can be used as a reference for the design of plant slope protection and reinforcement.
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