# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 2 Materials and Methods  
### Datasets  
Two datasets were used in the presented study: (i) a dataset, formed by combining images from two public datasets, used for detection performance comparison of models; and (ii) a private dataset, used for evaluating the generalization ability of models. The first dataset contains 2625 high-quality corn leaf disease and pest images, covering eight common categories of diseases and pests during corn plant growth, namely the red spider, corn borer, armyworm, aphids, peach borer, gray leaf spot, northern leaf blight, and northern leaf spot. The corn leaf disease part of the formed dataset was selected from the CD&S public dataset [32]. The pest part of the formed dataset was selected from the IP102 public dataset [33]. The dataset contains images collected from multiple sources includingfield collection and web-based gathering, covering different growth stages and various environmental conditions, which guarantees diverse and representative samples. Samples of eight common categories of corn leaf diseases and pests in this dataset, used for detection performance comparison of models, are shown in Figure 1. The eight selected disease and pest categories represent common problems in corn cultivation, providing significant practical value for agricultural applications, establishing a robust data foundation for research on intelligent detection of corn diseases and pests. Moreover, these offer excellent scalability and significant practical value. To achieve effective model training and evaluation, an 8:1:1 ratio was utilized in splitting the formed dataset, used for detection performance comparison of models, into a training subset consisting of 2100 images (80%), a validation subset with 262 images (10%), and a test subset including 263 images (10%). The statistics of images in each category and each subset of this dataset are shown in Table 1. The utilized division scheme ensures balanced distribution of different disease and pest categories in each subset. It also guarantees sufficient training data and objective test evaluation. The training subset was used for model parameter learning and feature extraction. The validation subset was used for hyperparameter tuning and model selection. The test subset was used for unbiased evaluation of the performance of the final model. The other dataset, i.e., the private one, was used for evaluating the generalization ability of models and their practical application potential. This dataset contains 3242 images, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & \begin{tabular}{c} **Number of Images** \\ **in Training Subset** \\ \end{tabular} & \begin{tabular}{c} **Number of Images** \\ **in Validation Subset** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Images** \\ **in Test Subset** \\ \end{tabular} &
\begin{tabular}{c} **Total Number** \\ **of Images** \\ \end{tabular} \\ \hline Red spider & 125 & 20 & 15 & 160 \\ Corn borer & 339 & 42 & 43 & 424 \\ Armyworm & 163 & 22 & 21 & 206 \\ Aphids & 696 & 84 & 94 & 874 \\ Peach borer & 193 & 26 & 22 & 241 \\ \hline Gray leaf spot & 213 & 26 & 23 & 262 \\ Northern leaf blight & 207 & 18 & 24 & 249 \\ Northern leaf spot & 164 & 24 & 21 & 209 \\ \hline
**Total Number of Images** & **2100** & **262** & **263** & **2625** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of images in each category and each subset of the dataset, used for detection performance comparison of models. Figure 1: Samples of eight common categories of corn leaf diseases and pests in the dataset, used for detection performance comparison of models: (**a**) Red spider; (**b**) Corn borer; (**c**) Armyworm; (**d**) Aphids; (**e**) Peach borer; (**f**) Gray leaf spot; (**g**) Northern leaf blight; (**h**) Northern leaf spot. covering six categories of diseases and pests, including fall armyworm larva, yellow stem borer larva, yellow stem borer, maize streak disease, leaf rust, and gray leaf spot.