## 2 Materials and Methods  
### Dataset, Data Augmentation and Experimental Setup  
The PlantDoc dataset [6] was utilized for plant leaf disease detection. It comprises 2570 images, representing 13 plant species categorized into 27 distinct classes--10 representing healthy specimens and 17 denoting various disease states. The distribution of these images is depicted in Figure 1. Of the total, 1798 images were designated for model training, with 386 images allocated to the test and validation sets. The validation set is instrumental in fine-tuning the model parameters and mitigating overfitting during the training phase, whereas the test set is employed to evaluate the model's efficacy on new, previously unseen data. As illustrated in Figure 2, which is reproduced from the PlantDoc dataset paper [6], the image shows example samples from a subset of plant disease categories. The dataset includes both diseased and healthy leaf images from multiple crop species, highlighting the inter-class diversity and visual complexity relevant to the plant disease detection task. To improve data diversity and strengthen the model's generalization and robustness, extensive data augmentation techniques were employed on the 1798 training images. These techniques were specifically chosen to address key challenges in agricultural imaging:* Horizontal/Vertical flipping: Simulates natural variations in leaf orientation. * Affine transformations: Compensates for perspective distortions from different camera angles. * Gaussian noise (\(\sigma^{2}=1\)): Enhances resilience to sensor noise and lighting variations. The selection was based on three principles: geometric invariance (robustness to scale/rotation), photometric realism (mimicking real-world degradation), and class balance mitigation (reducing bias toward majority classes). Following these enhancements, the augmented training set expanded to include 8990 images. Quantitative evaluation based on our ablation study(Table 1) shows significant improvements: WD-YOLO with augmentation achieved 65.4% mAP50 (+1.6% over WD-noDA), 60.1% recall (+0.7%), and 33% reduction in false positives (0.8 vs. 1.2 per image). The impact of these data augmentation processes is illustrated in Figure 3, with each technique annotated to highlight its functional purpose. The PlantDoc dataset is publicly available at: [[https://github.com/pratikkayal/PlantDoc-Dataset](https://github.com/pratikkayal/PlantDoc-Dataset)]([https://github.com/pratikkayal/PlantDoc-Dataset](https://github.com/pratikkayal/PlantDoc-Dataset)) (accessed 16 August 2024). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **mAP50 (\%)** & **mAP50-95 (\%)** & **P (\%)** & **R (\%)** & **Params (M)** \\ \hline YOLOv10n & 56.3 & 47.9 & 56.8 & 55.4 & 2.71 \\ YOLO-DA & 58.8 & 48.0 & 59.7 & 56.9 & 2.71 \\ YOLO-DS & 61.2 & 50.3 & 59.8 & 58.7 & 2.78 \\ YOLO-WT & 60.3 & 50.0 & 58.4 & 57.9 & 2.72 \\ WD-noDis & 62.3 & 51.4 & 59.6 & 58.8 & 2.78 \\ WD-noDA & 63.8 & 52.8 & 60.0 & 59.4 & 2.78 \\ WD-YOLO & 65.4 & 53.1 & 60.4 & 60.1 & 2.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation analysis of component combinations in WD-YOLO. Figure 1: Statistics of the PlantDoc dataset. Figure 2: Representative samples from PlantDoc dataset. The experimental environment was established using Python 3.8.0 and PyTorch 1.12.1. Computational tasks were executed on an 3090 graphics card, which operated under CUDA version 11.3. During the training of our proposed model, early stopping was implemented as the termination criterion to prevent overfitting by monitoring the validation loss. The training regimen was initially configured to span up to 500 epochs. However, it was prematurely terminated if there was no improvement in the validation loss over a span of 10 consecutive epochs. To address hardware requirements and field applicability, we conducted comprehensive benchmarks comparing WD-YOLO with YOLOv8 and EfficientNetV2 on resource-constrained devices.