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This study presents a novel approach to address challenges regarding data acquisition for object detection and tracking purposes by enhancing UAV path planning specifically designed for fruit detection in woody crops trained on vertical trellises, considering the biophysical environment of the field. The proposed method implements the Ant Colony Optimization (ACO) algorithm and enables single and multiple UAVs to fly synchronously while ensuring a safe distance between platforms. The results highlight that ACO is able to generate optimal and safe routes, considering the vegetation and covering the whole agricultural area. Moreover, it shows potential to solve partial leaf occlusion for fruit identification. [ABSTRACT FROM AUTHOR]

Precision agriculture has seen significant advancements with the integration of remote-sensing technologies. However, challenges such as real-time data availability and computing limitations persist. This study aimed to develop a standardized method for generating spatial variability maps for vineyard management using UAV (unmanned aerial vehicle) imagery. Using IDW (inverse distance weight), nadir images with geotagged locations were processed to extract spectral information. The results were analyzed using the NGRDI (normalized green-red difference index) and demonstrated that geo-interpolation methods are effective compared to traditional photogrammetry-based methods but 90% faster, highlighting their potential in real-time applications and edge computing. In addition, IDW correlation with Sentinel-2 imagery reached values as high as r = 0.8. This method offers a faster, less resource-intensive alternative to existing techniques for crop mapping, addressing the current challenges in precision agriculture. [ABSTRACT FROM AUTHOR]

The use of satellite data in precision agriculture, especially for woody crops, has gained prominence. Such data aid in forecasting yield, predicting crop quality, and irrigation management by providing insights into crop conditions. However, the accuracy of parameters such as the Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) derived from satellites is questioned. This research compares Sentinel-2 satellite imagery with LiDAR data and multispectral imagery gathered over a vineyard in northern Spain using Unmanned Aerial Vehicles (UAVs) in July, August, and September 2022, focusing on veraison, a key stage in precision viticulture. The findings reveal a moderate correlation between satellite and UAV NDVI values (up to R2 = 0.6) but a discrepancy in leaf area estimations from satellite imagery, suggesting its limited use for such applications in hedgerow systems in agriculture. Nonetheless, satellite data's ability to detect crop spatial variability remains useful for field management. The study emphasizes the potential benefits and drawbacks of diversifying remote sensing techniques for effective agricultural management. [ABSTRACT FROM AUTHOR]