## 1 Introduction  
Agriculture has been the platform from which human sustainability evolves over time, even with the spontaneous increase in population in recent time to over seven billion [1]. The activities of farming have changed, and are still changing, from the known traditional ways to a more sophisticated approach with the aid of evolving technology [2]. Planting one kind of crop on a single area of land is changing to a system of mixed cropping on the same area of land (mixed farming), which is gradually becoming a common practice [3]. Mapping out crops in mixed farming for effective management to meet the purpose of better harvest is a crucial component of precision agriculture. Therefore, the task of optimized multi-crop classification in mixed farms has been a subject of interest for many agricultural researchers worldwide [4]. This task involves the identification and classification of multiple crop types in a single agricultural field. The classification of multiple crops in mixed farming systems is a challenging yet vital task which provides insights into the dynamics of crop combinations and their impact on yields and sustainability [5]. While early studies on multi-crop classification focused on traditional rule-based methods and required enormous resources and time to execute [6], with advancements in remote sensing technologies, machine learning, and deep learning the approach to this task has evolved significantly [7]. Modern techniques, including the use of satellite imagery, drones, and deep learning algorithms, have revolutionized multi-crop classification because these methods offer higher accuracy and scalability [8]. The benefits of accurate crop classification are multifaceted. It enables optimized resource allocation, where farmers can efficiently distribute resources such as water, nutrients, and pesticides to specific crops based on their individual needs. This approach supports precision agriculture practices, allowing for site-specific management and data-driven decision-making [9]. Furthermore, accurate classification aids in yield prediction, helping farmers to estimate and plan for future harvests more accurately [10]. Nonetheless, crop classification comes with its own set of challenges. Crops evolve over time, and change in appearance as they progress through different growth stages. This can make classification difficult, especially if the same field is used to plant different crops in various seasons. Additionally, mixed planting nearby can lead to overlapping canopies, making it more challenging to identify and classify crops accurately [11]. A range of tools and technologies are available for crop classification in mixed farming. High-resolution satellite imagery can provide an overview of the entire farm, assisting in large-scale classification [12]. Drones equipped with cameras or sensors can capture detailed images of crops from a closer vantage point. Spectral imaging, such as hyperspectral or multispectral sensors, can capture the unique spectral signatures of different crops, aiding in their identification [2]. In recent years, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have become useful models in precision agriculture [7]. They can distinguish between different crops and even assess their health and growth stage based on training data [2, 12]. YOLOv8 is a popular CNN architecture released on 10 January 2023, and has shown promising results in object detection and classification [13]. It is a rapid, precise, and user-friendly model designed for tasks such as object detection, segmentation, classification, and pose estimation. However, the performance of YOLOv8, like other architectures, is highly dependent on hyperparameters, which means that the choice of hyperparameters can significantly affect its performance. The algorithm has several hyperparameters that can be tuned to improve its performance, including the learning rate, batch size, number of epochs, and number of anchors.