Search Results (3)

Imagery from unoccupied aerial vehicles (UAVs) is useful for mapping floating and emerged primary producers, as well as single taxa of submerged primary producers in shallow, clear lakes and streams. However, there is little research on the effectiveness of UAV imagery-based detection and quantification of submerged filamentous algae and rooted macrophytes in deeper rivers using a standard red-green-blue (RGB) camera. This study provides a novel application of UAV imagery analysis for monitoring a non-wadeable river, the Klamath River in northern California, USA. River depth and solar angle during flight were analyzed to understand their effects on benthic primary producer detection. A supervised, pixel-based Random Trees classifier was utilized as a detection mechanism to estimate the percent cover of submerged filamentous algae and rooted macrophytes from aerial photos within 32 sites along the river in June and July 2019. In-situ surveys conducted via wading and snorkeling were used to validate these data. Overall accuracy was 82% for all sites and the highest overall accuracy of classified UAV images was associated with solar angles between 47.5 and 58.72° (10:04 a.m. to 11:21 a.m.). Benthic algae were detected at depths of 1.9 m underwater and submerged macrophytes were detected down to 1.2 m (river depth) via the UAV imagery in this relatively clear river (Secchi depth > 2 m). Percent cover reached a maximum of 31% for rooted macrophytes and 39% for filamentous algae within all sites. Macrophytes dominated the upstream reaches, while filamentous algae dominated the downstream reaches closer to the Pacific Ocean. In upcoming years, four proposed dam removals are expected to alter the species composition and abundance of benthic filamentous algae and rooted macrophytes, and aerial imagery provides an effective method to monitor these changes. Full article

Members of the Fusarium oxysporum species complex include pathogenic and non-pathogenic isolates and infect a broad range of plant species. F. oxysporum f. sp. cubense (Foc) causes the destructive Fusarium wilt of banana, and the recently emerged Foc tropical race 4 strain threatens the global banana industry. Secreted in xylem (SIX) genes encode for F. oxysporum effector proteins that are associated with virulence in pathogenic F. oxysporum, however they have rarely been reported from non-pathogenic F. oxysporum isolates. Our recent survey of asymptomatic banana plants grown in Foc-infested fields in Queensland and northern NSW revealed that diverse Fusarium spp, including F. oxysporum, reside in the plant roots and pseudostem without causing obvious damage to the plant. Intriguingly, we amplified SIX genes from several of the putative endophytic F. oxysporum isolates identified in the survey and found that they differ in their profile to known Foc SIX genes. To study the role of the endophytic F. oxysporum isolates in planta and the biological function of their SIX genes in more detail, we will re-inoculate cultivated and wild diploid banana lines with the endophytic F. oxysporum strains under glasshouse conditions to assess if they are non-pathogenic on banana. Secondly, we will determine whether the endophytic F. oxysporum SIX genes are expressed in planta and/or in vitro and look at the transcriptome changes occurring in the host following infection. Finally, endophytic F. oxysporum strains transformed with GFP will be used to investigate the extent of fungal colonisation in the plant. Full article

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations. Full article