# Monitoring Safron Crops with UAVs
## 2 Related Work
In Smart Farming, UAVs can also facilitate crop irrigation processes [8, 9] toward achieving significant water conservation, while also being able to detect areas in cultivation with higher irrigation needs. Such processes can support the farmer to save time and increase crop productivity and quality. Furthermore, UAVs in cooperation with image processing tools can generate specialized maps that display the morphology of the soil, thus supporting the more efficient irrigation planning of each crop separately. Following the same principles, UAVs can leverage crop spraying procedures [10], by applying chemicals in a timely and highly spatially resolved manner. Via a smart monitoring system for Smart Farming, crop fields can be efficiently studied regarding their morphology and crop heights, enabling the deployment of a spray management plan that covers the specific needs of each cultivation. Based on this plan, UAVs will be able to spray the appropriate amount of herbicide spatially, while also adjusting the amount of pesticide depending on the crop site in which it is located. UAVs are also recommended for monitoring crops that require specific environmental conditions and parameters so as to be cultivated. One of these crops is saffron, which is mainly cultivated in Iran, which has 90% of the world's production, as well as in Greece, Spain, India, Morocco, and Italy [11]. The saffron cultivation is a quite demanding and delicate process requiring the deployment of tailor-based technologies and continuous crop monitoring to avoid animal and disease interventions [12]. Up until now, only a few studies have been focused on smart monitoring of saffron crops. In Reference [13], the authors discussed the applicability of Wireless Sensor Networks (WSNs) for saffron and cultivation in Afghanistan. In Reference [14], the findings are presented for mapping saffron across larger areas, as well as for monitoring changes in saffron distribution. Another recent study focused on the estimation of land area under cultivation of saffron by using time difference method and satellite imagery [15]. In contrast to previous studies, our work presents a complete overview of a methodology for monitoring fields that supports several options. In particular, the main differences with existing works are summarized as follows:  
* This study _presented, applied, and evaluated_ a methodology that incorporates _all the steps of the field monitoring process_, including the remote data collection step with the appropriate equipment, the photo-processing step, the application of machine learning algorithms step, the application of the estimation models step, and finally to the step of the evaluation. * In contrast to previous research, the target of this study was twofold: (a) The first target was to _estimate a variety of field attributes_ (i.e., weeds, flowers, and animal intrusions). The target was to upgrade profit earnings by the adoption of new technologies, ensuring flower picking at an appropriate time in a proper collection material at an appropriate age [12]. Moreover, animal intrusions in the fields, causing a huge loss in production, will be prevented, and weeds will be detected in time. (b) The second target was to _examine the applicability of a variety of machine learning methods_ to farming estimations and draw conclusions based on their accuracy. * To suggest software and technologies that are of limited cost, since the methodology followed relies on tools that are open source [16; 17; 18].