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This paper presents a modular and holistic Precision Agriculture platform, named CoFly, incorporating custom-developed AI and ICT technologies with pioneering functionalities in a UAV-agnostic system. Cognitional operations of micro Flying vehicles are utilized for data acquisition incorporating advanced coverage path planning and obstacle avoidance functionalities. Photogrammetric outcomes are extracted by processing UAV data into 2D fields and crop health maps, enabling the extraction of high-level semantic information about seed yields and quality. Based on vegetation health, CoFly incorporates a pixel-wise processing pipeline to detect and classify crop health deterioration sources. On top of that, a novel UAV mission planning scheme is employed to enable site-specific treatment by providing an automated solution for a targeted, on-the-spot, inspection. Upon the acquired inspection footage, a weed detection module is deployed, utilizing deep-learning methods, enabling weed classification. All of these capabilities are integrated inside a cost-effective and user-friendly end-to-end platform functioning on mobile devices. CoFly was tested and validated with extensive experimentation in agricultural fields with lucerne and wheat crops in Chalkidiki, Greece showcasing its performance.

The CoFly-WeedDB contains 201 RGB images (∼436 MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The 1280 × 720 RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to –87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5 m and 3 m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.