Multi-class detection of cherry tomatoes using improved Yolov4-tiny model

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high‐quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43–1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky–Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full‐band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS‐SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC‐(CARS‐SPA)‐GA‐SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS‐SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA‐SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

Introduction: As the third largest food crop in the world, maize has wide varieties with similar appearances, which makes identification difficult. To solve the problem of identification of hybrid maize varieties, a method based on hyperspectral image technology combined with a convolutional neural network (CNN) is proposed to identify maize varieties.Methods: In this study, 735 maize seeds from seven half-parent hybrid maize varieties were regarded as the research object. The maize seed images in the range of 900 ~ 1700nm were obtained by hyperspectral image acquisition system. The region of interest (ROI) of the embryo surface was selected, and the spectral reflectance of maize seeds was extracted. After Savitzky-Golay (SG) Smoothing pretreatment, Maximum Normalization (MN) pretreatment was performed. The 56 feature wavelengths were selected by Competitive Adaptive Reweighting Algorithm (CARS) and Successive Projection Algorithm (SPA). And the 56 wavelengths were mapped to high-dimensional space by high-dimensional feature mapping and then reconstructed into three-dimensional image features. A five-layer convolution neural network was used to identify three-dimensional image features, and nine (SG+MN)-(CARS+SPA)-CNN maize variety identification models were established by changing the input feature dimension and the depth factor size of the model layer.Results and Discussion: The results show that the maize variety classification model works best, when the input feature dimension is 768 and the layer depth factor d is 1.0. At this point, the model accuracy of the test set is 96.65% and the detection frame rate is1000 Fps/s in GPU environment, which can realize the rapid and effective non-destructive detection of maize varieties. This study provides a new idea for the rapid and accurate identification of maize seeds and seeds of other crops.

Intelligent management of large-scale farms necessitates efficient monitoring of individual livestock. To address this need, a three-phase intelligent monitoring system based on deep learning was designed, integrating a multi-part detection network for flock inventory counting, a facial classification model for facial identity recognition, and a facial expression analysis network for health assessment. For multi-part detection network, The YOLOv5s path aggregation network was modified by incorporating a multi-link convolution fusion block (MCFB) to enhance fine-grained feature extraction across objects of different sizes. To improve the detection of dense small targets, a Re-Parameterizable Convolution (RepConv) structure was introduced into the YOLOv5s head. For facial identity recognition, the sixth-stage structure in GhostNet was replaced with a four-layer spatially separable self-attention mechanism (SSSA) to strengthen key feature extraction. Additionally, model compression techniques were applied to optimize the facial expression analysis network for improved efficiency. A transfer learning strategy was employed for weight pre-training, and performance was evaluated using FPS, model weight, mean average precision (mAP), and test set accuracy. Experimental results demonstrated that the enhanced multi-part identification network effectively extracted features from different regions of the sheep flock, achieving an average detection accuracy of 95.84%, with a 2.55% improvement in mAP compared to YOLOv5s. The improved facial classification network achieved a test set accuracy of 98.9%, surpassing GhostNet by 3.1%. Additionally, the facial expression analysis network attained a test set accuracy of 99.2%, representing a 3.6% increase compared to EfficientNet. The proposed system significantly enhances the accuracy and efficiency of sheep flock monitoring by integrating advanced feature extraction and model optimization techniques. The improvements in facial classification and expression analysis further enable real-time health monitoring, contributing to intelligent livestock management.

Corrigendum: Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution