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Challenges in Flight Planning for Automatic Inspection of Olive Groves using UAVs

Vladan Papić, Mirela Kundid Vasić, Toma Sikora
20th February 2025
1st International Conference on Drones and Unmanned Systems, Granada, Spain

In this paper, an overview of the challenges for the Unmanned Aerial Vehicles path planning for the inspection of olive orchards will be given. Standard approaches, limitations and requirements for specific types of applications such as olive fruit detection and yield prediction are analyzed. Possible flight planning approaches are compared along with their advantages and disadvantages. An optimal solution is proposed and expressions for estimating the recorded canopy area of an individual tree are derived. The values calculated in this way using the described fruit detection method allow for the estimation of the total yield.

Evaluation of Deep Neural Network Architectures and Image Datasets for Olive Fruit Detection

Josip Musić, Toma Sikora, Mirjana Bonković, Vladan Papić
20th September 2025
SoftCOM 2025

Accurate fruit yield estimation is increasingly recognized as a vital component of sustainable agricultural practices, particularly in economically and culturally significant crops like olives. As the global demand for olive oil rises, so does the need for precise yield forecasting, which can lead to better resource allocation and enhanced management practices. The advent of machine learning (ML), particularly deep neural networks (DNNs) and computer vision, has revolutionized agricultural yield estimation practices. 
Thus, in this paper, we explore several state-of-the-art DNN architectures and their performance on two databases: our custom one (with 11,227 olive instances) and a database available from the literature (with 245,089 olive instances). Obtained results (mAP50 ranging from 68% to 95% depending on the used architecture) demonstrate the applicability of the DNN-based approaches in the complex and difficult task of olive detection from high-resolution images, while highlighting the importance of database size and quality (diversity) on DNN's final performance. 

Dataset: https://zenodo.org/search?q=Split_Olive_DATASETv1&l=list&p=1&s=10&sort=bestmatch

Optimizing Olive Detection via YOLOv8 and Active Learning: Benefits of Uncertainty-Based and Missed-Detection Sampling Strategies

Mirjana Bonković, Ozana Uvodić, Mojmil Cecić, Ana Kuzmanić Skelin
20th September 2025
SoftCOM 2025

Accurate detection and localization of olives in orchard environments is essential for precision agriculture, enabling tasks such as yield estimation, automated harvesting, and crop monitoring. While YOLOv8 provides robust object detection capabilities, optimizing its performance for olive detection remains challenging due to small object sizes, visual variability, and labeling costs. This paper explores the benefits of integrating active learning with YOLOv8, focusing on uncertainty-based sampling and novel missed-detection strategies. We propose and evaluate three acquisition methods—missed ground-truth detection, low-confidence sampling and a small-size (fuzzy score) sampling - to minimize annotation effort while maximizing detection accuracy. Experimental results show that careful hyperparameter tuning, combined with a strategic active learning pipeline, can improve mean Average Precision (mAP) by 7–10 percentage points and reduce labeling effort by 25–35%. Our findings provide practical insights for deploying efficient olive detection systems in real-world agricultural contexts. 

Monocular Depth Estimation Driven Canopy Segmentation for Enhanced Determination of Vegetation Indices in Olive Grove Monitoring

Vladan Papić, Nediljko Bugarin, Ivana Marin, Sven Gotovac, Josip Gugić
19th September 2025
Remote Sensing

This study investigates the application of unmanned aerial vehicles with multispectral cameras for monitoring the condition of olive groves with high accuracy. Multispectral images of olive groves provided detailed insight into the spectral data required for the analysis of vegetation indices. Using deep learning-based object detection, individual olive trees were identified within the images, which allowed the extraction of parts corresponding to each tree. To separate the background from the canopy, segmentation based on the monocular depth estimation algorithm, Depth Anything, was applied. In this way, elements that are not part of the tree’s crown were removed for more accurate analysis and calculation of the NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) indices. The obtained results were compared with the results obtained for unsegmented patches, threshold-based patches, and manually segmented patches. The comparison and analysis carried out shows that the proposed segmentation approach improved the accuracy of NDVI and NDRE by focusing exclusively on the crowns of the observed trees, excluding the noise of the surrounding vegetation and soil. In addition, measurements were carried out on three observed olive groves at different parts of the vegetation cycle, and the values of the vegetation indices were compared. This integrated method combining drone-based multispectral imaging, deep learning object detection, and advanced segmentation techniques highlights a robust approach to olive tree health monitoring and provides insight into seasonal vegetation dynamics, for winter and spring, to capture differences in vegetative activity.