# A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
## 1 Introduction  
Apples are one of the most popular fruits worldwide [1]. According to statistics from the China Apple Industry Association (www.chinaapple.org.cn), in 2022, global apple production reached 81.578 million tons, with China contributing 47.5718 million tons, which accounts for over 50% of the worldwide total. Apple cultivation is widespread, with China, the United States, and Poland as major producers [2]. However, apple leaf diseases significantly impact apple quality and yield [3, 4]. These diseases can cause leaves to dry and fall off, hindering fruit development and flower bud formation, weakening the tree. The consequences of these diseases lead to decreased income for agricultural workers and hamper the apple industry's growth. Detecting apple leaf diseases in a timely and efficient manner becomes crucial in improving the remuneration of farm workers and maintaining the industry's development [5]. Traditionally, manual detection is the primary method of detecting apple leaf diseases and pests, requiring skilled researchers or agricultural workers to assess with the naked eye. This approach is relatively straightforward for handling small fruit trees but impractical for large-scale cultivation, potentially resulting in subjective biases and inadequate assessment [6]. Therefore, a fast and accurate method of detecting apple leaf pests and diseases isnecessary. Visual computing and advanced machine learning have significantly boosted agriculture, with applications such as precision agriculture management, disease detection, and crop phenotyping [7; 8]. [PERSON] et al. [9] achieved up to 93% accuracy in identifying plant diseases by using image processing techniques to analyze plant disease images. They combined the K-means clustering algorithm with neural networks for feature extraction and used Support Vector Machines (SVMs) as classifiers for disease identification and classification. Similarly, [PERSON] et al. [10] employed a range of supervised machine learning techniques, including Naive Bayes (NB), Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and Random Forest (RF) to detect maize diseases through plant images. They conducted a comparative analysis to determine which model achieved the highest prediction accuracy. Among all the models compared, the Random Forest algorithm performed optimally, with 79.23% accuracy, showing its efficiency in predicting maize diseases. [PERSON] et al. [11] successfully identified apple Alternaria leaf blotch with a detection accuracy of 99.1%, using 11 image features and an MLP pattern classifier. However, despite the remarkable results of these studies regarding recognition accuracy, some limitations and challenges still need to be addressed. Light, background, and weather variations in field conditions can significantly affect image quality and representation of disease characteristics, thereby reducing the model's accuracy and reliability. In recent years, convolutional neural networks (CNNs) have made impressive strides in various applications [12; 13], surpassing traditional machine-learning techniques. A network model proposed by [PERSON] et al. [14] employed two pre-trained CNNs, EfficientNetB0 and DenseNet121, to distinguish between healthy and diseased corn plant leaves. The deep features extracted from each CNN were combined using a joining technique to create a more complex feature set, resulting in a 98.56% classification accuracy. In another study, [PERSON] et al. [15] employed a hybrid ResNet50 and VGG16 features model to detect corn leaf diseases. By utilizing migration learning and image enhancement techniques, they achieved 99.65% accuracy. [PERSON] et al. [16] proposed an integrated model that merges single-stage and two-stage target detection networks. Utilizing YOLO as its foundation, the single-stage network refines its internal structure. In contrast, the two-stage network, leveraging Faster-RCNN, integrates a clustering algorithm during candidate frame generation to enhance the detection of small targets.