With the continuous innovation and development of technologies for breeding varieties of fruits, there are more than 8000 varieties of apples in existence. The accurate identification of apple varieties can promote the healthy and stable development of the global apple industry and protect the breeding property rights of rights-holders. To avoid economic losses due to the improper identification of varieties at the seedling-procurement stage, this paper proposes the classification of varieties using images of apple leaves in conjunction with the network models of traditional classification methods, supplemented with deep-learning methods, such as AlexNet, VGG, and ResNet, to account for their shortcomings in robustness and generalizability. We used the Multi-Attention Fusion Convolutional Neural Network (MAFNet) classification method for apple leaf images. The convolutional block distribution pattern of [2,2,2,2] is used to drive the feature extraction layer to have a symmetric structure. According to the characteristics of the dataset, the model is based on the ResNet model to optimize the feature extraction module and integrate a variety of attention mechanisms to achieve the weight distribution of channel features, reduce the interference information before and after feature extraction, complete the accurate extraction of image features, from low-dimensional to high-dimensional, and finally obtain the apple classification results through the Softmax function. The experiments were conducted on a mixture of leaves from 30 apple varieties at 2 growth stages: tender and mature. A total of 14,400 images were used for training, 2400 for validation, and 7200 for testing. The model’s classification accuracy was 98.14%, which improved the accuracy and reduced the classification imputation time as compared with the previous model. Among them, the accuracy rate of “Red General”, “SinanoGold”, and “Jonagold” reached 100%, and the accuracy rate of the bud variant of the Fuji line (“Fuji 2001”, “Red General”, “Yanfu 0”. and “Yanfu 3”) also had an accuracy rate of over 90%. The method proposed in this paper not only significantly improves the classification accuracy of apple cultivars, but it also achieves this with a low cost and a high efficiency level, providing a new way of thinking and an essential technical reference for apple cultivar identification by growers, operators, and law enforcement supervisors in the production practice.

The innovation of germplasm resources and the continuous breeding of new varieties of apples (Malus domestica Borkh.) have yielded more than 8000 apple cultivars. The ability to identify apple cultivars with ease and accuracy can solve problems in apple breeding related to property rights protection to promote the healthy development of the global apple industry. However, the existing methods are inconsistent and time-consuming. This paper proposes an efficient and convenient method for the classification of apple cultivars using a deep convolutional neural network with leaf image input, which is the delicate symmetry of a human brain learning. The model was constructed using the TensorFlow framework and trained on a dataset of 12,435 leaf images for the identification of 14 apple cultivars. The proposed method achieved an overall accuracy of 0.9711 and could successfully avoid the over-fitting problem. Tests on an unknown independent testing set resulted in a mean accuracy, mean error, and variance of ${\mu }_{acc}=0.9685$ , ${\mu }_{\epsilon }=0.0315$ , and ${\sigma }^2=1.89025E-4$ , respectively, indicating that the generalization accuracy and stability of the model were very good. Finally, the classification performance for each cultivar was tested. The results show that model had an accuracy of 1.0000 for Ace, Hongrouyouxi, Jazz, and Honey Crisp cultivars, and only one leaf was incorrectly identified for 2001, Ada Red, Jonagold, and Gold Spur cultivars, with accuracies of 0.9787, 0.9800, 0.9773, and 0.9737, respectively. Jingning1 and Pinova cultivars were classified with the lowest accuracies, with 0.8780 and 0.8864, respectively. The results also show that the genetic relationship between cultivars Shoufu 3 and Yanfu 3 is very high, which is mainly because they were both selected from a red mutation of Fuji and bred in Yantai City, Shandong Province, China. Generally, this study indicates that the proposed deep learning model is a novel and improved solution for apple cultivar identification, with high generalization accuracy, stable convergence, and high specificity.

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.

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed’s nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the Rp2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

Corn as a key food crop, has a wide range of varieties with similar appearances, making manual classification challenging. Thus, fast and non-destructive seed variety identification is crucial for improving yield and quality. Hyperspectral imaging is commonly used for non-destructive seed classification. For the advancement of smart agriculture and precision breeding, in this study, 30 corn varieties from Northwest China were analyzed using hyperspectral images (870–1709 nm) to extract spectral reflectance from the embryonic region. Traditional methods often involve selecting specific bands, which can lead to information loss and limited variety selection. In this study, information loss was reduced and manual intervention was minimized by using full-band spectral data. And preprocessing is performed using first-order derivatives to reduce the interference of noise and irrelevant information. Classification experiments were conducted using KNN, ELM, RF, 1DCNN, and an improved 1DCNN-LSTM-ATTENTION-ECA (CLA-CA) model. The CLA-CA model achieved the highest classification accuracy of 95.38%, significantly outperforming traditional machine learning and 1DCNN models. It is demonstrated that the innovative module combination method proposed in this study is able to successfully classify varieties of corn seeds, which provides a new option for the rapid and non-destructive identification of a variety of corn seeds.

Wheat is a significant cereal for humans, with diverse varieties. The growth of the wheat industry and the protection of breeding rights can be promoted through the accurate identification of wheat varieties. To recognize wheat seeds quickly and accurately, this paper proposes a convolutional neural network-based image-recognition method for wheat seeds, namely GC_DRNet. The model is based on the ResNet18 network and incorporates the dense network idea by changing its residual module to a dense residual module and introducing a global contextual module, reducing the network model’s parameters and improving the network’s recognition accuracy. Experiments were conducted on the self-constructed wheat-seed dataset and the publicly available dataset CIFAR-100 by combining GC_DRNet with network models such as ResNet18, ResNet34, ResNet50, and DenseNet121. The GC_DRNet model achieved a recognition accuracy of 96.98% on the wheat-seed dataset, which was improved by 2.34%, 1.43%, 2.05%, and 1.77% compared to ResNet18, ResNet34, ResNet50, and DenseNet121, respectively. On the CIFAR-100 dataset, the recognition accuracy of the GC_DRNet model was 80.77%, which improved the accuracy of ResNet18, ResNet34, ResNet50, and DenseNet121 by 8.19%, 1.6%, 9.59%, and 16.29%, respectively. Analyzing the confusion-matrix results of the wheat-seed dataset, the average recognition precision of the test set was 97.02%, the recall rate was 96.99%, and the F1 value was 96.98%. The parameter size of the GC_DRNet model was smaller than that of the other three models, only 11.65MB. The experimental results show that the GC_DRNet has a high level of recognition accuracy and detection capability for images of wheat seeds and provides technical support for wheat-seed identification.