Proximal Active Optical Sensing Operational Improvement for Research Using the CropCircle ACS-470, Implications for Measurement of Normalized Difference Vegetation Index (NDVI).

Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.

Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance.

The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R 2 > 0.7 in ∼34% of the metabolites) and predicted (Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.

Arthropod Demography, Distribution, and Dispersion in a Novel Trap-Cropped Cotton Agroecosystem.

Vernonia [Vernonia galamensis (Cass.) Less.] (Asterales: Asteraceae) was examined as a potential trap crop for the cotton (Gossypium hirsutum L., Malvales: Malvaceae) arthropod complex. Four rows of vernonia were embedded within a 96-row cotton field. The abundance of true bug pests, true bug predators, and spiders were determined by whole-plant and sweep net sampling procedures during the early, middle, and late phases of the cotton-growing season. The census data showed that the arthropods had a strong preference for the vernonia trap crop throughout the cotton-growing season. The movement of the arthropods from the trap crop into cotton was also measured using the protein immunomarking technique as a mark-capture procedure. The arthropods inhabiting the vernonia trap crop were marked directly in the field with a broadcast spray application of egg albumin (protein) during each phase of the study. In turn, the captured specimens were examined for the presence of the mark by an egg albumin-specific enzyme-linked immunosorbent assay. Very few marked specimens were captured beyond the vernonia trap crop 1, 3, and 6 d after each marking event. The arthropods' strong attraction and fidelity to vernonia indicate that it could serve as a trap crop for cotton pests and a refuge for natural enemies.

Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting.

BACKGROUND: Photosynthesis is one of the most important biological reactions and forms the basis of crop productivity and yield on which a growing global population relies. However, to develop improved plant cultivars that are capable of increased productivity, methods that can accurately and quickly quantify photosynthetic efficiency in large numbers of genotypes under field conditions are needed. Chlorophyll fluorescence imaging is a rapid, non-destructive measurement that can provide insight into the efficiency of the light-dependent reactions of photosynthesis. RESULTS: To test and validate a field-deployed fluorescence imaging system on the TERRA-REF field scanalyzer, leaves of potted sorghum plants were treated with a photosystem II inhibitor, DCMU, to reduce photochemical efficiency (FV/FM). The ability of the fluorescence imaging system to detect changes in fluorescence was determined by comparing the image-derived values with a handheld fluorometer. This study demonstrated that the imaging system was able to accurately measure photochemical efficiency (FV/FM) and was highly correlated (r = 0.92) with the handheld fluorometer values. Additionally, the fluorescence imaging system was able to track the decrease in photochemical efficiency due to treatment of DCMU over a 7 day period. CONCLUSIONS: The system's ability to capture the temporal dynamics of the plants' response to this induced stress, which has comparable dynamics to abiotic and biotic stressors found in field environments, indicates the system is operating correctly. With the validation of the fluorescence imaging system, physiological and genetic studies can be undertaken that leverage the fluorescence imaging capabilities and throughput of the field scanalyzer.

A data workflow to support plant breeding decisions from a terrestrial field-based high-throughput plant phenotyping system.

Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.

Deploying a Proximal Sensing Cart to Identify Drought-Adaptive Traits in Upland Cotton for High-Throughput Phenotyping.

Field-based high-throughput phenotyping is an emerging approach to quantify difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts represent an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and specifically a deployment protocol, were developed to phenotype traits related to drought tolerance in the field. The cart-sensor package included an infrared thermometer, ultrasonic transducer, multi-spectral reflectance sensor, weather station, and RGB cameras. The cart deployment protocol was evaluated on 35 upland cotton (Gossypium hirsutum L.) entries grown in 2017 at Maricopa, AZ, United States. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87 hectare field averaged 2 h and 27 min and produced 50.7 MB and 45.7 GB of data from the sensors and RGB cameras, respectively. Canopy temperature, crop water stress index (CWSI), canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) differed among entries and showed an interaction with the water regime (p < 0.05). Broad-sense heritability (H2) estimates ranged from 0.097 to 0.574 across all phenotypes and collections. Canopy cover estimated from RGB images increased with counts of established plants (r = 0.747, p = 0.033). Based on the cart-derived phenotypes, three entries were found to have improved drought-adaptive traits compared to a local adapted cultivar. These results indicate that the deployment protocol developed for the cart and sensor package can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.

Use of Body-Mounted Cameras to Enhance Data Collection: An Evaluation of Two Arthropod Sampling Techniques.

A study was conducted that compared the effectiveness of a sweepnet versus a vacuum suction device for collecting arthropods in cotton. The study differs from previous research in that body-mounted action cameras (B-MACs) were used to record the activity of the person conducting the arthropod collections. The videos produced by the B-MACs were then analyzed with behavioral event recording software to quantify various aspects of the sampling process. The sampler's speed and the number of sampling sweeps or vacuum suctions taken over a fixed distance (12.2 m) of cotton were two of the more significant sampling characteristics quantified for each method. The arthropod counts obtained, combined with the analyses of the videos, enabled us to estimate arthropod sampling efficiency for each technique based on fixed distance, time, and sample unit measurements. Data revealed that the vacuuming was the most precise method for collecting arthropods in the relatively small cotton research plots. However, data also indicates that the sweepnet method would be more efficient for collecting most of the cotton-dwelling arthropod taxa, especially if the sampler could continuously sweep for at least 1 min or ≥80 m (e.g., in larger research plots). The B-MACs are inexpensive and non-cumbersome, the video images generated are outstanding, and they can be archived to provide permanent documentation of a research project. The methods described here could be useful for other types of field-based research to enhance data collection efficiency.

Resistance to Multiple Soil-Borne Pathogens of the Pacific Northwest, USA Is Colocated in a Wheat Recombinant Inbred Line Population.

Soil-borne pathogens of the Pacific Northwest decrease yields in both spring and winter wheat. Pathogens of economic importance include Fusarium culmorum, Pratylenchus neglectus, P. thornei, and Rhizoctonia solani AG8. Few options are available to growers to manage these pathogens and reduce yield loss, therefore the focus for breeding programs is on developing resistant wheat cultivars. A recombinant inbred line population, LouAu (MP-7, NSL 511036), was developed to identify quantitative trait loci (QTL) associated with resistance to P. neglectus and P. thornei This same population was later suspected to be resistant to F. culmorum and R. solani AG8. This study confirms partial resistance to F. culmorum and R. solani AG8 is present in this population. Six major and 16 speculative QTL were identified across seven measured traits. Four of the six major QTL were found within the same genomic region of the 5A wheat chromosome suggesting shared gene(s) contribute to the resistance. These QTL will be useful in breeding programs looking to incorporate resistance to soil-borne pathogens in wheat cultivars.

Detection and Discrimination of Pratylenchus neglectus and P. thornei in DNA Extracts from Soil.

A species-specific polymerase chain reaction (PCR) method was developed to detect and identify the root-lesion nematodes Pratylenchus neglectus and P. thornei from soil. A primer set was designed from Pratylenchus 28S rRNA gene sequences of the D3 expansion domain. Primer specificity was confirmed with 23 isolates of 15 nematode species and other plant-parasitic and non-plant-parasitic nematodes typically present in the soil communities, and with six fungal species commonly associated with wheat root rot. DNA obtained using a commercially available kit and a method developed in our laboratory gave comparable amplification. PCR conditions were optimized and the two species were differentiated by PCR products of 144 bp for P. neglectus and 288 bp for P. thornei. With this assay, we detected a single juvenile in 1 g of sterile, inoculated soil. Examination of 30 field soil samples revealed that this method was applicable to a range of soils naturally infested with these two pathogens in Oregon. This PCR-based method is rapid, efficient, and reliable, does not require expertise in nematode taxonomy and morphology, and could be used as a rapid diagnostic tool for commercial and research applications for disease forecasting and management.