Precision agriculture workflow, from data collection to data management using FOSS tools: An application in Northern Italy vineyard

Abstract

In the past decades, technology-based agriculture, also known as precision agriculture (pa) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as uavs (unmanned aerial vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. Pa needs software to store and process spatial data and the free and open software system (foss) community kept pace with pa’s needs: Several foss software tools have been developed for data gathering, analysis, and restitution. The adoption of foss solutions, webgis platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing uav data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the pa’s data elaboration and management with a foss approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in pa activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide uav and pa users with a foss-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally foss workflow to visualise, process, analyse, and manage pa data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage very high resolution (vhr) agricultural multispectral-derived information gathered by uav systems. A vineyard in northern italy is used as an example to show the workflow of data generation and the data structure of the web tool. A uav survey was carried out using a six-band multispectral camera and the data were elaborated through the structure from motion (sfm) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of gis-developed algorithms allowed individual tree detection (itd) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3d webgis, allowing users to update the spatial database. The webgis can be operated from web browsers and desktop gis. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.