Introduction: The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions Captures: The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training. Dataset: The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic. Zip Folder: The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic. Article Information: The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset. Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). Farm-flow dataset: Intrusion detection in smart agriculture based on network flows, Computers and Electrical Engineering, Volume 121, 109892, DOI: 10.1016/j.compeleceng.2024.109892