Wheat ear counting is crucial for calculating wheat phenotypic parameters and scientifically managing fields, which is essential for estimating wheat field yield. In wheat fields, detecting wheat ears can be challenging due to factors such as changes in illumination, wheat ear growth posture, and the appearance color of wheat ears. To improve the accuracy and efficiency of wheat ear detection and meet the demands of intelligent yield estimation, this study proposes an efficient model, Generalized Focal Loss WheatNet (GFLWheatNet), for wheat ear detection. This model precisely counts small, dense, and overlapping wheat ears. Firstly, in the feature extraction stage, we discarded the C4 feature layer of the ResNet50 and added the Convolutional block attention module (CBAM) to this location. This step maintains strong feature extraction capabilities while reducing redundant feature information. Secondly, in the reinforcement layer, we designed a skip connection module to replace the multi-scale feature fusion network, expanding the receptive field to adapt to various scales of wheat ears. Thirdly, leveraging the concept of distribution-guided localization, we constructed a detection head network to address the challenge of low accuracy in detecting dense and overlapping targets. Validation on the publicly available Global Wheat Head Detection dataset (GWHD-2021) demonstrates that GFLWheatNet achieves detection accuracies of 43.3% and 93.7% in terms of mean Average Precision (mAP) and $A{P}_{50}$ (Intersection over Union (IOU) = 0.5), respectively. Compared to other models, it exhibits strong performance in terms of detection accuracy and efficiency. This model can serve as a reference for intelligent wheat ear counting during wheat yield estimation and provide theoretical insights for the detection of ears in other grain crops.

Corn leaf pest and disease detection techniques play a vital role in enabling precise pesticide application and optimizing disease management strategies. However, traditional machine learning methods require manual feature design and exhibit limited generalization capabilities. While early deep learning models improved automation, they struggled to handle the complexity of diverse pest and disease features effectively. ​The primary objective of this study is to address these limitations by developing a novel detection model, Gsconv-Adaptive Feature Fusion Network (GAF-Net), which enhances the accuracy and robustness of corn pest and disease detection. Built upon YOLOv8, GAF-Net incorporates the adaptive spatial feature fusion strategy and the Grouped Spatial Convolution (GSConv) module, which significantly improve multiscale spot feature integration and effective processing of damaged leaf regions. ​This innovation enables GAF-Net to achieve superior performance in complex agricultural environments. To overcome the scarcity of high-quality datasets, we constructed an integrated image dataset encompassing four major pest and disease features: Northern corn leaf blight, pest damage, brown spot, and downy mildew. Experimental results demonstrate that GAF-Net achieves outstanding performance, with a mean average precision (mAP)_50 score of 80.8% (a 4.1% improvement over the baseline model) and an mAP (50–95) score of 52.6% (a 3.3% enhancement). These quantified results highlight the efficacy of GAF-Net in addressing the challenges of corn pest and disease detection, making it a valuable tool for precision agriculture.