Selected work

Representative Papers

Monitoring digestate application on agricultural crops using Sentinel-2 Satellite imagery

Apr 28, 2025

This study addresses environmental risks associated with agricultural application of digestate—including soil health degradation, microplastic contamination, and nitrogen leaching—by proposing a dynamic monitoring framework integrating Sentinel-2 time-series remote sensing and machine learning. We systematically construct the first crop-specific spectral response dataset (encompassing EOMI, NDVI, and EVI) following digestate application across four major crops: wheat, maize, sunflower, and sugar beet. A hybrid remote sensing–machine learning detection architecture is developed, incorporating Random Forest, k-Nearest Neighbors, Gradient Boosting, and Feedforward Neural Networks. Applied at scale in Thessaly, Greece, the method enables large-area, low-cost, and near-real-time identification of digestate presence, achieving a maximum F1-score of 0.85. This approach overcomes the spatial limitations and high operational costs of conventional field-based monitoring, establishing a novel paradigm for precision organic fertilizer management and intelligent environmental risk control.

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Assessing the Capability of YOLO- and Transformer-based Object Detectors for Real-time Weed Detection

Jan 29, 2025

To address the need for real-time, precise crop–weed discrimination in agricultural fields to minimize herbicide usage, this study systematically evaluates YOLOv8/v9/v10 and RT-DETR models on a unified, real-world field dataset for fine-grained plant recognition—specifically, single-species identification combined with plant-type classification (crop vs. monocot/dicot weeds). A two-stage annotation strategy ensures fair cross-model comparison, and inference latency is empirically measured on an RTX 4090 GPU. Results show that YOLOv9s/e achieves the best overall trade-off (mAP: 79.86%, recall: 72.36%), while RT-DETR-l attains the highest accuracy (mAP: 81.46%). Lightweight variants such as YOLOv10n achieve 7.58 ms/frame, balancing real-time performance and practical utility. The study reveals architecture–task alignment principles for fine-grained agricultural detection and provides empirically grounded, deployable model selection guidelines for precision spraying systems.

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Recent publications

Latest Papers

Monitoring digestate application on agricultural crops using Sentinel-2 Satellite imagery

Apr 28, 2025

This study addresses environmental risks associated with agricultural application of digestate—including soil health degradation, microplastic contamination, and nitrogen leaching—by proposing a dynamic monitoring framework integrating Sentinel-2 time-series remote sensing and machine learning. We systematically construct the first crop-specific spectral response dataset (encompassing EOMI, NDVI, and EVI) following digestate application across four major crops: wheat, maize, sunflower, and sugar beet. A hybrid remote sensing–machine learning detection architecture is developed, incorporating Random Forest, k-Nearest Neighbors, Gradient Boosting, and Feedforward Neural Networks. Applied at scale in Thessaly, Greece, the method enables large-area, low-cost, and near-real-time identification of digestate presence, achieving a maximum F1-score of 0.85. This approach overcomes the spatial limitations and high operational costs of conventional field-based monitoring, establishing a novel paradigm for precision organic fertilizer management and intelligent environmental risk control.

0 citationsRead paper

Assessing the Capability of YOLO- and Transformer-based Object Detectors for Real-time Weed Detection

Jan 29, 2025

To address the need for real-time, precise crop–weed discrimination in agricultural fields to minimize herbicide usage, this study systematically evaluates YOLOv8/v9/v10 and RT-DETR models on a unified, real-world field dataset for fine-grained plant recognition—specifically, single-species identification combined with plant-type classification (crop vs. monocot/dicot weeds). A two-stage annotation strategy ensures fair cross-model comparison, and inference latency is empirically measured on an RTX 4090 GPU. Results show that YOLOv9s/e achieves the best overall trade-off (mAP: 79.86%, recall: 72.36%), while RT-DETR-l attains the highest accuracy (mAP: 81.46%). Lightweight variants such as YOLOv10n achieve 7.58 ms/frame, balancing real-time performance and practical utility. The study reveals architecture–task alignment principles for fine-grained agricultural detection and provides empirically grounded, deployable model selection guidelines for precision spraying systems.

0 citationsRead paper