Pest infestations remain a critical threat to global agriculture, significantly compromising crop yield and quality. While accurate pest detection forms the foundation of precision pest management, current approaches face two primary challenges: (1) the scarcity of comprehensive multi-scale, multi-category pest datasets and (2) performance limitations in detection models caused by substantial target scale variations and high inter-class morphological similarity. To address these issues, we present three key contributions: First, we introduce Insect25—a novel agricultural pest detection dataset containing 25 distinct pest categories, comprising 18,349 high-resolution images. This dataset specifically addresses scale diversity through multi-resolution acquisition protocols, significantly enriching feature distribution for robust model training. Second, we propose GC-Faster RCNN, an enhanced detection framework integrating a hybrid attention mechanism that synergistically combines channel-wise correlations and spatial dependencies. This dual attention design enables more discriminative feature extraction, which is particularly effective for distinguishing morphologically similar pest species. Third, we implement an optimized training strategy featuring a cosine annealing scheduler with linear warm-up, accelerating model convergence while maintaining training stability. Experiments have shown that compared with the original Faster RCNN model, GC-Faster RCNN has improved the average accuracy mAP0.5 on the Insect25 dataset by 4.5 percentage points, and mAP0.75 by 20.4 percentage points, mAP0.5:0.95 increased by 20.8 percentage points, and the recall rate increased by 16.6 percentage points. In addition, experiments have also shown that the GC-Faster RCNN detection method can reduce interference from multiple scales and high similarity between categories, improving detection performance.

Paddy rice grain variety classification is essential for quality control, as different rice varieties exhibit significant variations in quality attributes, affecting both food security and market value. The integration of hyperspectral imaging with machine learning presents a promising approach for precise classification, though challenges remain in managing the high dimensionality and variability of spectral data, along with the need for model interpretability. To address these challenges, this study employs a CNN-Transformer model that incorporates Standard Normal Variate (SNV) preprocessing, Competitive Adaptive Reweighted Sampling (CARS) for feature wavelength selection, and interpretability analysis to optimize the classification of hybrid indica paddy rice grain varieties. The results show that the CNN-Transformer model outperforms baseline models, achieving an accuracy of 95.33% and an F1-score of 95.40%. Interpretability analysis reveals that the model’s ability to learn from key wavelength features is significantly stronger than that of the comparison models. The key spectral bands identified for hybrid indica paddy rice grain variety classification lie within the 400–440 nm, 580–700 nm, and 880–960 nm ranges. This study demonstrates the potential of hyperspectral imaging combined with machine learning to advance rice variety classification, providing a powerful and interpretable tool for automated rice quality control in agricultural practices.

In the field of smart plant protection, accurate early monitoring of winged aphids is critical, as it enables the interruption of viral disease transmission and reduces dependence on pesticides. In response to the core challenges of low efficiency in manual counting associated with current sticky trap-based monitoring, as well as the insufficient recognition accuracy and poor robustness of computer vision models in dense small-target scenarios, this study aims to develop a high-precision, highly reliable automated identification method for winged aphids. To achieve this, a specialized detection model named PCSNet is proposed. Based on YOLOv12, this model innovatively incorporates a coordinate attention mechanism to enhance the perception of spatial structures for small targets. Simultaneously, a shallow feature enhancement branch (SFEB) is introduced to enrich detailed information, and the Normalized Wasserstein Distance loss function is integrated to optimize bounding box regression. Comparative experiments conducted on a self-constructed dataset of sticky trap images encompassing complex field backgrounds demonstrate that the PCSNet model achieves optimal detection performance, with a mean average precision (mAP) of 0.791 and a precision of 0.866, significantly outperforming mainstream detection models and various attention mechanism variants. This research provides an effective technical solution for constructing a real-time and automated intelligent pest monitoring system, offering substantial application value for advancing the intelligent transformation of pest and disease monitoring and promoting practices in green prevention and control.