# Monitoring Safron Crops with UAVs
## 1 Introduction  
**Smart Farming (SF)** is a concept that essentially represents the application of advanced Information and Communication Technologies (ICTs) into agriculture to manage farming operations in real time. Smart Farming mainly depends on IoT (Internet of Things) technologies such as Wireless Sensor Networks (WSNs), unmanned aerial vehicles (UAVs), cloud services, and big data analytics, aiming to eliminate the need of physical work, to increase crop yields, and to reduce the environmental impact of irrational input usage. SF is considered to be the evolution of Precision Agriculture (PA), whose aim is to manage agricultural operations according to the estimated variability of crops based on spatial and temporal data. Some of the Smart Farming applications are as follows:  
* Autonomous vehicles and robots that can assist or replace the manual labor. * Automated control of inputs such as water and fertilizers to lower the cost for the farmers and help protect the environment. * Remote sensing technologies, such as sensors, UAVs, and satellites, that can be used for monitoring and managing soil, water, and other factors of production. These technologies can help toward the identification of factors that are stressing the crops, such as soil moisture, climate conditions, etc. * Machine learning techniques and big data analytics that can be adopted to analyze the large amount of data collected in order to detect potential threats to plants, such as weeds, animals, or diseases. * Computerized applications to create precision farming plans, field maps, crop logs, and yield maps. This allows for more precise application of inputs such as pesticides, herbicides, and fertilizers, helping to reduce costs, achieve higher yields, and practice more environmentally friendly agriculture. This technology is not yet widespread in Europe, so there are cultivars such as Kozani saffron to which it has not yet been applied. Saffron is an expensive spice used in various fields, such as cooking, pharmaceutics, and perfumery. More specifically, Greek saffron is grown only in the Kozani region in Western Macedonia and is an important source of income for local producers. This fact was a great incentive to launch the project DIAS ([[http://dias-project.gr/](http://dias-project.gr/)]([http://dias-project.gr/](http://dias-project.gr/)), accessed on 5 May 2022) for piloting the application of SF technologies in saffron cultivation in order to help producers reduce production costs and labor and increase their profits and product quality. The application of SF technologies is still in the research stage, so there is no standardization of the methods yet. Due to this fact, we initially performed a review of related pilot studies, examined the hardware options (UaV and sensor technologies) for monitoring remotely the fields, and selected the appropriate equipment. Finally, we evaluated the relevant data and image-analysis techniques to be applied. The lessons learned during the DIAS project are documented in this study, in which we proposed a process that employs UAVs to collect images from saffron fields and applied photogrammetry techniques and machine learning analysis to produce estimation models to detect saffron flowers, weeds, and signs of unwanted mammals. The aforementioned process consists of two main parts:  
(a) UAV photogrammetry methods for image acquisition and processing: Photogrammetry is the derivation of precise measurements from photographs. It involves taking a series of overlapping photographs of an object, building, person, or environment and converting them into a 2D or 3D model, using various computer algorithms. The photos can be taken either from the ground or from the air, as in this case from UAVs. (b) Machine learning techniques for model training: As for the machine learning part, for comparison purposes, we chose two different photo-analysis methods whose products are used as input to a machine learning algorithm: pixel-based and object-based analysis.