HERSS-MAIZE 2024; High-rEsolution dRone dataset for Site-Specific weed wanagement in Maize (San Piero - Pisa, Tuscany, Italy)

Weed management (WM) is recognised as one of the major challenges in modern agriculture, particularly within the framework of the Farm to Fork strategy, which aims to reduce pesticide use by 50% by 2030 while maintaining high crop productivity. Precision agriculture techniques, and especially Site-Specific Weed Management (SSWM), have emerged as promising approaches to achieve these goals by optimising post-emergence herbicide application according to the spatial distribution of weeds. The SSWM process is characterised by three fundamental phases. The weed detection (WD) process, the estimation of potential damage (PD), and the precision herbicide application (PHA) are supported by advanced sensing, imaging, and artificial intelligence (AI) methods.
The HERSS-MAIZE dataset was collected over a maize experimental field measuring approximately 0.8 ha, located in San Piero (Pisa, Tuscany, Italy), during the crop's critical period of susceptibility to weed competition. A total of thirteen UAV surveys were conducted using two different drone platforms—DJI MINI 3 PRO and DJI Mavic 3M—to compare data quality and assess the feasibility of SSWM under varying technological conditions. All acquired images were ortho-rectified to produce 13 high-resolution orthomosaics, representing temporal variations in vegetation cover and weed development. The dataset also incorporates flight metadata, sensor characteristics, and georeferencing information, thereby facilitating integration into Geographic Information System (GIS) environments or utilisation in machine learning and deep learning workflows for weed detection and crop monitoring.
The primary objective of the HERSS-MAIZE dataset is to provide a high-resolution, multi-temporal UAV-based resource for developing, validating, and benchmarking algorithms and methodologies for Weed Detection and Site-Specific Weed Management in maize. The incorporation of data from both professional and consumer-grade UAVs within the dataset facilitates research into scalable, cost-effective precision agriculture solutions that are aimed at reducing herbicide use and enhancing environmental sustainability. The objective of HERSS-MAIZE is to facilitate advancements in the following areas: AI-driven weed mapping, adaptive management zone delineation, and precision spraying strategies aligned with sustainable agricultural practices.