Using drone-retrieved multispectral data for phenomic selection in potato breeding.

Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.

Effects of alpha lipoic acid supplementation on post-thaw quality of drone semen.

This study aimed to determine the effect of alpha-lipoic acid (ALA) on post-thaw quality of bee semen. In the study, semen from sexually mature drone were collected. A series of experiments were carried out in which the retrieved semen was diluted with diluents containing different ALA concentrations or without ALA supplement (control). Cryopreserved sperm were thawed, and evaluated for motility (phase-contrast microscope), plasma and acrosomal membrane integrity, mitochondrial membrane potential, and DNA fregmantation. The results obtained showed that the highest motility after thawing was observed in the groups containing ALA 0.25 mmol (P < 0.05). Likewise, plasma membrane integrity was found to be better preserved in the ALA 0.25 mmol-added group than in other groups. Acrosomal integrity were also higher in the ALA-containing groups than in the control group (P < 0.05). The results of this study show that ALA supplementation especially at 0.25 mmol improved post-thawed sperm motility, plasma membrane functionality, and mitochondrial membrane potantial quality of honeybee semen.

Spray performance and control efficacy against pests in paddy rice by UAV-based pesticide application: effects of atomization, UAV configuration and flight velocity.

BACKGROUND: Over the past decade, unmanned aerial vehicle (UAV) sprayers have emerged as valuable tools for pesticide application across various crops. Despite their increasing usage, the impact of several factors on spray performance and control efficacy in paddy fields warrants further investigation. This study examines atomization characteristics using a UAV spray test platform. Our evaluation of field spraying performance considers three UAV models, two nozzle types, two flight velocities, and adding methylated vegetable oil adjuvant (MVOA), in comparison with the electrical knapsack sprayer (EKS). RESULTS: Atomization characteristics demonstrated consistency within the downwash airflow field, but were influenced by spray solution, nozzle type, and spray pressure. The eight-rotor UAV sprayer excelled over the quad-rotor model in terms of spray deposition across both upper and lower rice canopies. The six-rotor UAV exhibited enhanced spray deposition, droplet density, and coverage at a flight velocity of 4 m s-1 . The choice of nozzle was pivotal; the flat fan nozzle produced finer droplets with desirable deposition and coverage, whereas the air-induction nozzle created larger droplets with consistent coverage at various flight velocities. Adding MVOA improved the physicochemical properties of the spray and its performance, yielding a more uniform distribution. When compared with the EKS, UAVs showed lower deposition but comparable spray penetration. Control efficacy with the UAV sprayer was less effective against Mythimna separata but achieved 81% efficacy against Laodelphax striatellus within 7 days. CONCLUSION: This study demonstrates that UAV sprayers, particularly when combined with tank-mix adjuvants and nozzle types, can be highly effective for controlling rice pests. © 2023 Society of Chemical Industry.

Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles.

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

CFD-based pesticide selection for a nozzle used in a six-rotor UAV in hover mode for tea spraying.

BACKGROUND: A key challenge for unmanned aerial vehicle (UAV) spraying sometimes used in tea plantations is the downwash flow structure there stronger than in crops. In addition, the UAV spray is affected by the relationship between the nozzle design and the pesticide. However, there is little current research on this aspect. As a preliminary step this study focuses on the most appropriate pesticide for a designated nozzle in a six-rotor UAV according to the nozzle-pesticide relationship using a three-dimensional computational fluid dynamics model. This model considers the downwash flow structure effect and nozzle spray performance in hover. Nozzle FVP110-02, widely used in six-rotor UAVs, is used as a representative nozzle and bifenthrin and tea saponin water, commonly used in tea plantations, are used as the pesticides. RESULTS: The downwash flow structure of the six-rotor UAV in hover was conveniently controlled by the flight height and rotational speed, thereby causing the turbulence to be more stable. For nozzle FVP110-02, bifenthrin was more appropriate than tea saponin water at the same concentration, whilst bifenthrin and tea saponin water at a concentration of 1:1000 showed the best performance under identical working conditions. CONCLUSION: The numerical model developed here was shown to be effective for investigating the relationship between nozzle and pesticide. Our findings will help to not only improve UAV spraying for tea cultivation but also provide guidelines for pesticide selection in crops. Further work will address the comparison of the rigorous qualification of the numerical simulations with the measurements by the field test. © 2023 Society of Chemical Industry.

Evaluating the effectiveness of drones for quantifying invasive upside-down jellyfish (Cassiopea sp.) in Lake Macquarie, Australia.

Upside-down jellyfish (Cassiopea sp.) are mostly sedentary, benthic jellyfish that have invaded estuarine ecosystems around the world. Monitoring the spread of this invasive jellyfish must contend with high spatial and temporal variability in abundance of individuals, especially around their invasion front. Here, we evaluated the utility of drones to survey invasive Cassiopea in a coastal lake on the east coast of Australia. To assess the efficacy of a drone-based methodology, we compared the densities and counts of Cassiopea from drone observations to conventional boat-based observations and evaluated cost and time efficiency of these methods. We showed that there was no significant difference in Cassiopea density measured by drones compared to boat-based methods along the same transects. However, abundance estimates of Cassiopea derived from scaling-up transect densities were over-inflated by 319% for drones and 178% for boats, compared to drone-based counts of the whole site. Although conventional boat-based survey techniques were cost-efficient in the short-term, we recommend doing whole-of-site counts using drones. This is because it provides a time-saving and precise technique for long-term monitoring of the spatio-temporally dynamic invasion front of Cassiopea in coastal lakes and other sheltered marine habitats with relatively clear water.

Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields.

Field phenotyping of crops has recently gained considerable attention leading to the development of new protocols for recording plant traits of interest. Phenotyping in field conditions can be performed by various cameras, sensors, and imaging platforms. In this chapter, practical aspects as well as advantages and disadvantages of aboveground phenotyping platforms are highlighted with a focus on drone-based imaging and relevant image analysis for field conditions. It includes useful planning tips for experimental design as well as protocols, sources, and tools for image acquisition, preprocessing, feature extraction, and machine learning highlighting the possibilities with computer vision. Several open and free resources are given to speed up data analysis for biologists.This chapter targets professionals and researchers with limited computational background performing or wishing to perform phenotyping of field crops, especially with a drone-based platform. The advice and methods described focus on potato but can mostly be used for field phenotyping of any crops.