# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 1 Introduction  
By 2050, the world's population is expected to grow significantly to nine billion people. To satisfy the expected demands, agricultural production must rise by almost 70%. The agricultural industry will nevertheless face several difficulties at that time, including a decline in the amount of arable land and the requirement for more intense production. Productivity will also be impacted by additional problems, such as climate change and water shortages [1].  
The productivity and quality of crops can be affected by prejudicial vegetation because of its ability to spread fast and unintentionally. In the battle for food, water, sunlight, and growth space, unwanted plants face up to crops. Numerous factors affect how control tactics are employed to lessen their impact [1].  
An over-dependence on herbicides can harm the environment, cause non-target crop damage, destroy natural flora and soil biodiversity, and be harmful to the general public's and farmworkers' health [2]. Herbicide administration may be decreased by 40% on average by employing extensive information on the types of weed plants that have emerged, their growth phases, and plant densities in a field [3].  
Due to rising labor expenses and an increase in public concern for their health and the environment, automation in weed management has gained popularity. Automated vegetation control methods may be useful from a sustainability and financial perspective [1]. With its continued development, artificial intelligence (AI) is finding more and more benefits in agriculture. The Internet of Things (IoT) and the 4 th Industrial Revolution (Industry 4.0) both provide new opportunities for innovation [4].  
Image classification, a subfield of computer vision, plays a crucial role in modernizing the agricultural industry. By utilizing advanced machine learning techniques, such as deep learning (DL) and AI, image recognition has the potential to transform traditional farming practices and enhance productivity [4]. Thus, the detection and classification of flora has been a focus of several machine learning (ML)-based algorithms' development, making it a potential topic of research [5]. DL techniques provide several benefits for picture classification, object identification, and recognition [1].  
In recent years, convolutional neural networks (CNNs) have emerged as a game-changing technology, revolutionizing the field of image classification. With their unique architecture and sophisticated algorithms, CNN models have propelled the accuracy and efficiency of image analysis to unprecedented levels [2]. A strong framework for creating, developing, and deploying neural network (NN) models is offered by PyTorch [1; 6].  
As we move forward into an increasingly visual world, the importance of CNN models for accurate image classification and understanding cannot be overstated, opening new possibilities and driving innovation across a wide range of domains. In this paper, a study about the different types of NN models for image classification available in PyTorch was conducted. The objective of this work was to study and understand how different models work as well as which one was the best, according to the experiments carried out. The proposal aims to discover which model is the best with both a small and large dataset.  
This paper is organized into four sections. Section 1 includes an introduction to the agricultural problem as well as a shortage of the suggested solution. Section 2 presents the literature related to the task of weed classification. Section 3 presents the materials and methods used in the suggested solution. Section 4 presents all the experiments and results achieved. Section 5 presents a discussion of the results, considering the groups of experiments carried out in Section 4. Finally, Section 6 presents the conclusions of this work.