Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Abstract

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.

Performance of neural networks for prediction of asparagine content in wheat grain from imaging data

“…The neural network model trained on the canopy data was not able to make similarly large increases in prediction of genotype over the GNB model, indicating that seed data is better suited for distinguishing genotypes than canopy measurements. This differs from the findings of Gao et al 13 , who found that images of wheat during tillering and flowering enabled more accurate classification of wheat genotypes than images of seeds. However, that study also used RGB images as input instead of decomposed spectral/morphological data (as is used in this study), which may account for some of these differences.…”

Crop Identification Using Deep Learning on LUCAS Crop Cover Photos

“…They furthermore used average accuracy as a metric over an imbalanced inference set, which is not in accordance with the literature [ 35 ]. Gao et al [ 36 ] achieved an accuracy of 99.51% in differentiating 30 wheat cultivars at the flowering (most mature) stage. This is very impressive, considering the present study suffered the most error when trying to discriminate between the various cereal classes.…”

“…When cereals are treated as a grouped cereal class, they are easily set apart from the rest of the crops, but when assessing the differences between the cereals, the structure of the fruit, stem, and leaf organs can look too similar. Indeed, the approach in Gao et al [ 36 ] yields such good results exactly because the model is designed to pick up on the subtle differences between the varieties. In the present case, grouping the cereals together would produce a M-F1 of 88.2 without and 90.4 with quadrant filtering, which is 12.5 and 14.7 points higher than the achieved result.…”

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

“…the best classification accuracy of 95.68% attributed to DensNet201 architectur Javanmardi et al [115] successfully classified nine corn seed varieties based on a VGGpre-trained CNN model. Gao et al [116] proposed a CNN based variety classificatio model for multiple growth periods of wheat. In the study, the CMPNet achieved hig classification precision at the seed stage of wheat (Specific performance index shown Figure 5) based on ResNet and SENet.…”