Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

Abstract

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.

Linear Transects of Imagery Increase Crop Monitoring Efficiency Using Fixed‐Wing Unmanned Aircraft Systems

“…Monitoring is not restricted just to analyzing changes in NDVI. At low altitudes, individual plants may be counted for calculation of germination rates, plant density, and plant survival (Gnädinger and Schmidhalter, 2017; Zhang et al, 2018; Buters et al, 2019). Plant cover may be calculated from the number of pixels with high NDVI (Hunt and Daughtry, 2018).…”

UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

“…Once the seedlings are well rooted in the soil, the post-plant monitoring phase starts, and the UAVs help keep track of the regeneration success and the seedling's growth consistently; for more information on the monitoring processes, including various stateof-art classification techniques, please refer to [32,[63][64][65]. Using spraying UAVs (e.g., DJI Agras), it is also possible to provide the water and nutrients (through fertilizers) that are crucial for the seedlings in their initial vegetation growth period.…”

“…In recent years, the advancement in the technology, machinery, and data processing related to unmanned aerial vehicles (UAVs; also known as drones) has revolutionized direct seeding operations [31][32][33][34]. Unlike the earlier aerial frameworks that focused solely on seed dispersals, the current UAV-powered endeavors emphasize optimizing locations for seed dispersal, manufacturing seed pods (or other vessels) for better seed germination, and monitoring seedling survival (more details in Section 3).…”

Dynamic Models and Control Techniques for Drone Delivery of Medications and Other Healthcare Items in COVID-19 Hotspots

“…UAV/drone surveying and mapping operations help in preparing 3D modeling for golf resort development and reconstruction. Drones can create GISbased tree management plans and generate 3D models showing investors what the resort will look like [35].…”