developing an intelligent detection method that can accurately detect eggplant diseases and is suitable for complex farmland environments is particularly important. In traditional methods of eggplant disease detection, manual inspection not only suffers from low efficiency but is also prone to the influence of subjective judgments made by the inspectors, making it unsuitable for large-scale detection tasks. To counteract these issues, researchers have explored automated and intelligent disease diagnosis approaches. Initial studies primarily hinged on spectral technology and machine learning algorithms; however, these methodologies exhibit inherent limits. [PERSON] et al. [2] combined visible and near-infrared reflectance spectroscopy with a back-propagation neural network (BP-NN) framework to facilitate the early identification of gray mold on eggplant foliage. They used principal component analysis (PCA) to reduce the dimensionality of the spectral data, Subsequently, they established a BP-NN prediction model based on extracted principal components, achieving an accuracy rate of 85%. Nonetheless, the high-dimensional and intricate nature of raw spectral data complicates model development. PCA may lose vital information when handling subtle spectral variations of specific diseases, thus compromising model prediction precision. [PERSON] et al. [3] extracted texture features from the acquired spectral images using gray level co-occurrence matrix (GLCM), then used k-nearest neighbors (KNN) and AdaBoost classification algorithms to classify healthy and diseased leaves, achieving a classification rate exceeding 88.46%. However, this process relies on manually extracted features, which can easily miss pixel-level information, causing the detection results to be sensitive to local changes and lacking robustness. Additionally, the computational complexity of the KNN and AdaBoost models is relatively high. The above methods involve a heavy workload and are difficult to promote in practical applications. With the continuous advancement of technology, researchers have embarked on a quest for more efficient and precise solutions. The rapid development of computer vision technology, particularly the emergence of deep learning algorithms, has offered new insights into plant disease detection. Deep learning algorithms have gradually demonstrated their unique advantages in the field of plant disease detection due to their capability for automatic feature extraction. [PERSON] et al. [4] leveraged a pre-trained VGG16 [5] model for feature extraction, which they coupled with a multi-class support vector machine (MSVM), to categorize diseases in eggplant leaves, attaining a remarkable classification accuracy of 99.4%. Nonetheless, the dataset used in their study was limited to laboratory conditions, which was insufficient to assess the generalization capability of the model. Likewise, [PERSON] et al. [6] devised an eggplant disease detection framework utilizing MobileNet V2 [7], tailored for mobile device compatibility. Regrettably, their dataset contained images with a low resolution of 224 \(\times\) 224 \(\times\) 3, restricting the system's capacity to discern subtle disease characteristics. The above studies primarily focused on classification tasks, which is inadequate for precise disease detection in real-world plantations, particularly when confronted with complex scenarios encompassing multiple disease types and potential overlap or occlusion of disease targets. Hence, to more accurately detect and classify eggplant diseases, especially in complex and ever-changing environments, a more advanced set of algorithms is needed. Compared to image classification algorithms, deep learning-based object detection algorithms, particularly the YOLO [8] series, have shown greater potential in plant disease detection. [PERSON] et al. [9] constructed the YOLO-Tea model by optimizing YOLOv5 [10], effectively addressing issues such as leaf shadows, illumination changes, and small object detection in tea photos, increasing mAP by 0.3% to 15.0%.