Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

Mechanized rice transplanting is a key method for large-scale cultivation. To enable rapid, accurate seedling detection and evaluate rice transplanting quality, this paper proposes an improved YOLOv12n model, YOLOv12n-SCA. First, this study embeds SCA hybrid attention modules into the YOLOv12n backbone and neck networks, integrating the advantages of SCSA (Spatial and Channel Synergistic Attention) and CA (Coordinate Attention) modules to enhance the model’s ability to extract and locate seedling features against complex paddy field backgrounds. Second, clustering analysis and a plant-spacing discrimination rule, based on seedling detection coordinates, automatically identify areas with missed transplanted seedlings. Experimental results show that the YOLOv12n-SCA model achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 of 93.6%, 90.3%, 95.3%, and 79.8%, respectively, with overall performance superior to that of the original YOLOv12n and single-attention-based improved models. In experiments on an independent dataset, the detection algorithm for rice seedlings in missed transplants achieved 82.34% accuracy and a counting error of 2.06%, indicating that it can effectively identify rice seedling locations and provide quality assessment results. These results show that YOLOv12n-SCA can support intelligent rice seedling detection, rice transplant quality evaluation, and smart field management in complex rice paddy environments.