Figure 25 shows the different velocities at each point ofthe peach and the tubes, however it is difficult to read the numbers from just the color on the peach. Using the probe gives the specific measurement at any point on the peach. The reading is made right before and right after the impact, at the same point. The simulation is paused before the impact to make the first reading and then after the impact to make the second reading. The comparisons between the simulation and the IRD is then shown in Figure 27. The numbers come from equation 1. Table 5 has the change in velocity for the IRD numbers, and the simulation numbers come from Table 8. The average error is 13.4% with a max error of 31.6% and a minimum error of 0.53%. Figure 26 shows the box plot for the change in energy recorded by the IRD. It shows that most of the data is very concentrated with only a few outliers. Figure 27, which uses the data from Figure 26 shows that the data is fairly accurate in comparison to the simulation data. The simulation was changed a few times in order to get these results. As previously mentioned the correct mesh sizing was found by decreasing the size of it until the results started to match up well to the IRD. Further decreasing the size of the mesh did not produce better results. The numbers start to deviate after about 8 inches. However, they continue to stay close to each other. The simulation line also follows the same trend line as the IRD, which is encouraging.The simulation did end up matching the IRD. This was one of the principal objectives of this research. The only part where the numbers do not match up exactly is at the middle heights.
The simulation continues to follow an upward trend whereas the IRD drops tail off for the middle heights . The simulation matches up closely to the IRD at the beginning and at the end. The IRD drops do not increase from 8 to 12 inches, the simulation does which is where the deviation starts to occur. However, dutch buckets for sale the IRD then jumps at the 20 inch drop and matches up well with simulation. The ANSYS simulation perfectly replicates the drop each time with the height being the only change, and in the real world, this is not possible to replicate. The IRD also drops at one point each time, but different things affect the result, in other word it is not as repeatable as the simulation. It lands on another spot of the shell, or the hose might not react the same way as before, and all of these factors lead to a range of values instead of just one value, like in the simulation. In a perfect world the simulation would perfectly match with the average IRD values however this is just not possible. That is why a box plot of values is given for the IRD. The IRD is less consistent because it is not a computer iterating values each time, it is just an accelerometer with no control system so the values will vary. Many IRD tests were conducted, not to try to match to the simulation results but to deliver consistent numbers. The simulation was then tuned to get to as close as possible, however because the IRD does not always follow a direct path but instead is a free drop, the simulation slightly diverts from the IRD. The important thing is that the numbers follow the correct trend line and that the results of the simulation fall within the values of the IRD drops. The average error is 13.4 % and most of the error comes from the two middle heights.The value of 0.25 shown in the simulation thus can be seen as one of the values were there is the most parity between the IRD and the simulation. However, as shown in Table 4 this value is well within the range of the IRD values, and actually one of the drops is exactly 0.25, but it is on the high end as shown in the box plot Figure 22.
The simulation is giving slightly higher values, however, the IRD and the simulation result for the 8 inch drop are pretty much the same number. The parity between the IRD and the simulation is negligible in these smaller drops, which is where most of the drops would occur in the real world. Each layer of the tree will receive a new layer of arms, so most of the drops will occur under 12 inches. With these simulation results, researchers can move forward with this design. The simulation numbers are accurate to what the IRD is showing. Another positive note is that since the simulation matches the IRD results, the values of g’s that the IRD is recording can be taken as real values. Even at a 20-inch drop, all of the g’s are under 25 g’s. There has also not been much research on the damage various fruits – cling peaches are of interest in this thesis – can absorb before they start experiencing actual damage. A few articles have generated some data for other fruits. Tomatoes, for example, start experiencing damage at 35 g’s Brecht . So, the benchmark should be around there for the peaches. The peaches should be good after going through this harvesting method since they are harder than tomatoes and thus their bruising threshold should be higher. The last result was to inspect the real peaches for damage. Since none of our peaches experienced that 35 g threshold, they should be fairly damage-free. And that is what the results show. There is no real correlation between the height at which the peaches were dropped and the damage to the peaches. However, this is a bit expected from the IRD and simulations. The velocity change is climbing the higher the drop is, but not by a huge amount that would benoticeable in a real fruit drop setting. The change in energy further demonstrates this, the IRD shows an almost flat line , until the 20-inch drop in which a peak does occur.
However, a 20 inch drop is not expected in a real world setting since most of the drops occur at around 12 inches. Since the IRD is not picking up a large change in energy, it is expected for the fruit to not have significant damage. In Figure 24, we can see a bit of a spike at the 12-inch height , and the amount of fruit damaged is at a max of 16 inches . However, there is no consistent increase in damage, whether the average size of the bruise, the amount of peaches bruised, or the maximum size of the bruise. This can, however, be taken as a positive in that our harvesting system will not cause damage to the fruit. The control was the fruit with the highest bruises, even though it never went through the arms. The harvester, in other words, did not cause more bruising than what the fruit had already experienced in the picking and ripening process. This means that the harvesting system will not damage the fruit more than human picking. So not only is it more cost effective since you don’t have to pay workers, but the fruit is picked with minimal damage compared to hand picking. In future work, the tunning of the simulation can be improved. As talked about in the results section the numbers started to deviate from the IRD and the simulation. Although most of the error can be attributed to the IRD, the simulation can also be further improved. One possible way of improving it is to simplify the geometry. The entire CAD model was used in this simulation, however, hydroponic net pots simplifying the parts that are simulated might give better and faster results. In this study we leveraged eQTL, GWAS and haplotype-resolved genome assemblies of a heterozygous octoploid to identify allelic variation in flavor genes and their regulatory elements. Finetuning of metabolomic traits such as amylose content in rice and sugar content in wild strawberry recently were made possible via CRISPR-Cas9 gene-editing technology. Similar approaches can be taken in cultivated strawberry for flavor improvement, but not before thebiosynthetic genes responsible for metabolites production and their regulatory elements are identified. Our pipeline has proven to be effective in identification of novel causal mutations for flavor genes responsible for natural variation in volatile content and can be further applied to various metabolomic and morphological aspects of strawberry fruit such as anthocyanin biosynthesis , sugar content and fruit firmness. These findings also will help breeders to select for genomic variants underlying volatiles important to flavor. New markers can be designed from regulatory regions of key aroma volatiles, including multiple medium-chain volatiles shown to improve strawberry flavor and consumer liking , methyl thioacetate contributing to overripe flavor and methyl anthranilate imparting grape flavor . In the present study, a new functional HRM marker for mesifurane was developed and tested in multiple populations . These favorable alleles of volatiles can be pyramided to improve overall fruit flavor via marker assisted selection. Strawberry also shares common volatiles with a variety of fruit crops. Specific esters are shared with apple , certain lactones are shared with peach and various terpenes are shared with citrus . Syntenic regions and orthologous genes could be exploited for flavor improvement in those species. Additional insights were gained for the strawberry gene regulatory landscape, SV diversity, complex interplays among cis- and trans- regulatory elements, and subgenome dominance.
Previously, Hardigan et al. and Pincot et al. showed a large genetic diversity existing in breeding populations of Fragaria × ananassa, challenging previous assumptions that cultivated strawberry lacked nucleotide variation owing to the nature of its interspecific origin and short history of domestication . Our work corroborated their findings and showed that even highly domesticated populations harbor substantial expression regulatory elements and structural variants. Over half of the expressed genes in fruit harbored at least one eQTL, and 22 731 eGenes had impactful cis-eQTL. The distribution of trans-eQTL is not random, but rather is concentrated at a few hotspots controlled by putative master regulators . The aggregation of trans-eQTL also was observed in plant species such as Lactuca sativa and Zea mays . Furthermore, we observed a substantial number of trans-eQTL among homoeologous chromosomes, similar to observations in other allopolyploid plant species . In cotton, physical interactions among chromatins from different subgenomes have been identified via Hi-C sequencing , supporting a potential regulatory mechanism among homoeologous chromosomes. However, owing to the high similarity among four subgenomes and limited length of Illumina reads, false alignment to incorrect homoeologous chromosomes could arise, leading to ‘ghost’ trans-eQTL signals. Future studies are needed to scrutinize the homoeologous trans-eQTL and investigate the mechanism behind this genome-wide phenomenon. Higher numbers of trans-eQTL in the Fragaria vesca-like subgenome are consistent with its dominance in octoploid strawberry . By contrast, the highly mixed Fragaria viridis- and Fragaria nipponica- like subgenomes contained much smaller numbers of trans-eQTL. The characterization of naturally-occurring allelic variants underlying volatile abundance has direct breeding applications. First, this will facilitate the selection of desirable alleles via DNA markers. Second, understanding the causal mutations in alleles can guide precision breeding approaches such as gene editing to modify the alleles themselves and/or their level of expression. From a broader perspective, multi-omics resources such as this one will have value for breeding a wide array of fruit traits. Enhancing consumer satisfaction in fruit ultimately will depend on the improvement of the many traits that together enhance the overall eating experience.Meeting the increasing demands for agricultural products while minimising negative impacts on biodiversity and ecosystem health is among the greatest global challenges . Intensive agricultural production and the simplification of agroecosystems threaten farmland biodiversity and associated ecosystem services worldwide . Concerns over loss of biodiversity and associated impairment of ecosystem services have helped strengthen the implementation of agri-environmental schemes and other measures to mitigate such negative consequences . Beyond restoration of farmland biodiversity in general, an implicit or explicit goal of such measures is to foster sustainable agricultural production through ecological intensification by harnessing biodiversity-based ecosystem services, such as crop pollination and natural pest control services .