# YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
## 1 Introduction  
Maize is widely cultivated around the world, and it has a high annual yield [1]. In China, it is the largest food crop, playing a crucial role in food processing, industrial materials, feed, and various other industries [2, 3, 4]. The leaves are the primary organs responsible for nutrient production in plants. Leaf diseases can directly impact both the yield and quality of maize. If these diseases are not managed in a timely manner, they can lead to reduced production at best, and widespread crop failure at worst, severely threatening national food security and social stability. In the maturity stage of maize, disease identification is still primarily conducted manually. Accurate detection typically requires sample collection for colony culture or artificial induction for verification, processes that are labor-intensive and time-consuming, making it challenging to control diseases quickly. Additionally, the limited availability of personnel and the distances involved often make rapid sample collection difficult. Unlike industrial products, agricultural products such as maize have lower appearance consistency and a wider variety of diseases, making traditional automated methods insufficient to replace manual labor. With the recent advancements in machine learning and artificial intelligence, machine vision technology based on Deep Neural Networks is increasingly being applied to the detection of maize leaf diseases. This technology can autonomously learn disease features, offering the potential to replace manual identification. Early research primarily focusedon image classification algorithms. For example, in 2020, [PERSON] and colleagues introduced the SCAM (Spatial Channel Attention Module) and Dense Transition Structure based on MobileNet V2, achieving an accuracy of 97.77% after 30 training rounds using transfer learning techniques [5]. In 2021, [PERSON] optimized the convolutional kernel size based on ResNet50 and improved the model training process by employing the Adam optimizer, L2 regularization, dropout strategy, and ReLU activation function, which led to faster convergence and better accuracy [6]. That same year, [PERSON] and colleagues proposed an image classification method for high-precision detection of corn leaf disease using a Multi-Activation Function (MAF) module. This module combined Sigmoid, ReLU, and tanh with Mish or LeakyReLU, resulting in an accuracy of 97.41% [7]. Additionally, [PERSON] and his team developed a dual-channel convolutional neural network that integrated VGG and ResNet, achieving a verification accuracy of 98.33%, significantly outperforming single models like ResNet50, VGG19, and AlexNet [8]. Beyond convolutional neural networks (CNNs), the Transformer algorithm has also been applied to build recognition networks. For instance, in 2022, [PERSON] and colleagues used a Transformer model combined with a self-attention mechanism to create a corn leaf disease recognition system that achieved an accuracy of 98.7% [9]. Recognition models based on image classification algorithms can achieve high-precision detection of specific corn leaf diseases during the maturity stage. However, these algorithms typically analyze entire images as single recognition objects, which limits them to identifying one sample or subject per photo. This becomes problematic when a photo contains multiple diseased leaves, as the algorithm cannot accurately differentiate or identify them. To address this limitation, many researchers have explored the use of target detection algorithms. In recent years, the YOLO (You Only Look Once) series of algorithms, which are based on convolutional neural networks (CNNs), have become the most popular choice for target detection in corn disease recognition. Despite their popularity, simply using these algorithms without modification often fails to achieve optimal results. Consequently, current research has focused on optimizing the basic network models to enhance detection speed and accuracy.