Conference Agenda

Spray drones or unmanned aerial spraying systems (UASS) have been homologated in Switzerland for seven years. It was the first European country to officially introduce these vehicles for plant protection purposes. To date, over 100 machines have passed the test. The transverse spray liquid distribution, measured by means of an enlarged patternator, showed coefficient of variation less than 15% at a working width of less than 4 m. Measurements of lateral wind speed generated at distances of 10–20 m showed low values of less than 2 m/s, which increased with heavier vehicles exceeding a weight of 150 kg. Flight accuracy, as measured by RTK-GNSS loggers, showed high accuracy, easily respecting the tolerance of ±50 cm for a planned flight route. The modified patternator and ultrasonic wind sensors proved valuable in characterising the working quality. The positive feedback over the past seven years confirms the pragmatic approach that was chosen. This has enabled the potential of UASS to be realised primarily in the cultivation of steep vineyards. A new trend is emerging of spreading slug pellets using these vehicles; however, initial results show that, like traditional methods, the UASS does not achieve regular distribution of the pellets on the ground.

Blackleg is a bacterial seed-borne disease that represents a major constraint in seed potato production, often causing substantial yield and quality losses. Field monitoring is still largely based on visual inspections, which are time-consuming, labor-intensive, and prone to subjectivity. This study presents an automated framework for identifying blackleg risk zones using Unmanned Aerial Vehicle (UAV) imagery and deep learning-based object detection.

High-resolution Red–Green–Blue (RGB) images were acquired through UAV surveys, and image frames were manually labeled to create a training dataset for a You Only Look Once (YOLO) model. To improve classification reliability, tractor wheel tracks and mechanically damaged plants were excluded from the annotations. The dataset was partitioned into training, validation, and testing subsets following a 70–15–15 ratio.

Experimental results indicate that the proposed method is capable of identifying areas potentially associated with blackleg symptoms under industrial farming conditions. The system supports spatial mapping of disease risk and may contribute to improved field monitoring practices.

The proposed approach has the potential to enhance monitoring efficiency, reduce dependency on manual scouting, and provide a scalable tool for supporting precision disease management in seed potato production.

Popillia japonica eradication measures are mandatory. Management currently relies on ground-based airblast broadcast spraying. Due to its biological behavior this approach is often inefficient. In this context, this study aimed to develop an alternative drone-based strategy for controlling P. japonica in vineyards, tailored to its biological characteristics. The approach consisted of three steps: (i) Unmanned Aerial Vehicle (UAV) monitoring, (ii) generation of insect distribution maps, and (iii) Unmanned Aerial Spray System (UASS) multitemporal spot spraying (i.e. only where and when needed). In 2025, vineyards were monitored using a Sentera Single NIR sensor installed on UAV. Images were processed with dedicated algorithms to map insects’ number and distribution. Insect clusters were defined and converted into prescription maps, which were uploaded to a UASS DJI AGRAS T25 for targeted spot-spraying. The strategy was tested at two locations, each including a UASS-managed and a conventionally treated reference plot (i.e. airblast sprayer). Results indicate that the novel developed strategy is effective in controlling adults of P. japonica. The monitoring algorithm overestimates approximately 15%, effectively quantifying insect number and distribution. On average, the multitemporal spot-spray approach achieved over 80% adult reduction, comparable to conventional reference spraying, while reducing insecticide use by at least 11%.

Rainfall instability in the Mediterranean region increasingly limits agricultural machinery trafficability during critical winter periods. In no-till systems, operating heavy machinery on waterlogged soil can cause irreversible structural damage and compaction. This study evaluates Unmanned Aerial Vehicles (UAVs) as a resilient alternative to conventional tractor-based nitrogen (N) top-dressing fertilisation. Field trials were conducted at Comenda Experimental Farm (INIAV Elvas innovation hub), Portugal, comparing a 4-tonne tractor equipped with a 600 L centrifugal spreader with a high-capacity 40 L UAV platform. During the fertilisation period, cumulative rainfall reached 188.4 mm, making soils inaccessible to ground equipment. The tractor treatment therefore received only the first nitrogen split (25 kg N ha⁻¹), while the UAV successfully applied the full fertilisation programme in two applications (25 + 25 kg N ha⁻¹). The UAV achieved a fertiliser distribution coefficient of variation below 15%, comparable to conventional methods, while eliminating soil contact. Multispectral analysis showed an average NDVI increase of 22% in UAV-managed areas, reflecting improved crop vigour due to optimal fertilisation timing. Biomass production was significantly higher in UAV-treated plots compared to tractor-limited treatments. These results demonstrate that UAV technology is a key tool for climate-resilient fertilisation, enabling precision agriculture under adverse field conditions.

Detasseling of female parent plants is a critical operation for ensuring seed purity and yield in hybrid maize seed production. While, missed tassels are inevitable after large-scale manual or ground-mechanical detasseling. This study proposes a UAV-based intelligent system for missed tassel detection and detasseling. Morphological and biomechanical characteristics of female-parent tassels at the detasseling stage were analyzed, defining three developmental stages and quantifying key physical and mechanical parameters to support detection and device design. For missed tassel detection, a multi-scenario vision framework was developed, including a high-altitude YOLO-MPT model for wide-area inspection and a lightweight MT-YOLO model for low-altitude real-time operation, achieving detection AP values above 93%. A geographic localization method based on orthophotos was established to enable precise coordinate mapping of detected tassels. The detasseling task was formulated as a traveling salesman problem, and a two-stage path-planning strategy combining K-means clustering and Held–Karp optimization was implemented. Based on biomechanical analysis, a cutting-based detasseling strategy was adopted, and a UAV-mounted detasseling device integrating a telescopic mechanism and rotary cutting blade was designed and optimized through structural simulation and CFD analysis. Field experiments demonstrated reliable centimeter-level positioning accuracy, a single-pass detasseling success rate of 87.5% (100% after secondary compensation).

The implementation of UAV-based precision fertilization represents a pivotal measure for optimizing agricultural productivity. However, the selection of UAV operational parameters currently lacks quantitative guidance, and the optimal settings remain ill-defined. This study investigated UAV-compatible fertilizer selection and operational parameter optimization. Physical properties of fertilizers were filtered out in light of their compatibility with the spreading mechanisms of unmanned aerial vehicles (UAVs), and a framework for optimizing UAV operational parameters was constructed by taking spreading uniformity as the core performance metric, via which the optimal operating parameters were defined. The experimental results indicated that the physical parameters of granular fertilizers have a notable influence on application quality, and key threshold values were identified: a moisture content of less than 0.5%, a sphericity of over 95%, and a density of 1.2~1.4 g/cm³. Moreover, calibrated application parameters for urea-ammonium nitrogen fertilizer were determined, including a flight height of 3 m, a flight speed of 2 m/s, and a spreader disc rotation speed of 900 r/min, which set benchmark values for UAV-based fertilization practices. Field trials further confirmed that UAV fertilization with optimized operational parameters boosted rice yields by 15.6%. This study puts forward an innovative approach to elevate the efficiency of fertilizer application.