Using Artificial Intelligence to Improve and Accelerate the Breeding Process for Root Structure Architecture in Alfalfa

Yields for alfalfa, the world’s most popular forage crop, have declined over the last ~50 years, and breeders, farmers, and other stakeholders are interested in breaking the yield bottleneck in innovative ways, such as with root system improvements and state-of-the-art methods including artificial intelligence (AI)

Looking at Cell Wall Components with Our Customers in Mind

Fiber digestibility of alfalfa for animal nutrition is a complex system encapsulating animal, plant, and microbe biological traits. Understanding all components within the system is key to predicting forage quality. We investigated the relationship between alfalfa cell wall components and invitro neutral detergent fiber digestibility (IVNDFD) speed (16-hr) and potential (96-hr) of by cattle ruminant microbes. A composite alfalfa (Medicago sativa L.) population from seven commercial cultivars underwent two cycles of bidirectional selection for plants with low or high stem 16-hr IVNDFD and low or high stem 96-hr IVNDFD. The resulting selected populations were then evaluated by near inferred spectrometry for structural cell wall components and thier relationship with IVNDFD. Hemi-cellulose and cellulose components were found to have a greater negative correlation (-0.85 & -0.86) on the speed of digestion (16-hr IVNDFD) than lignin (-0.70). Whereas, for the overall potential of stem digestibility, lignin (-0.89) had the greatest negative correlation. The relationship between cellulose and lignin with IVNDFD was futher supported with the use of a path model. Lignin and 96-hr IVNDFD had the strongest broad sense heritability across the populations (0.74 & 0.70 respectively). Pectin components correlated positively with speed of digestion (0.41) but had limited correlation on the overall digestibility potential. As IVNDFD increased with each breeding cycle, it remained stable across environments along with concentrations of total cell wall components, lignin, hemi-cellulose, and pectin. However, the cellulose concentrations were not stable across environments. Cell wall components such as hemi-cellulose and lignin could be used as selection traits for increased IVNDFD breeding and may be a way to link invitro digestibility to plant trait genes for genomic selection

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the “Second Green Revolution”. To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI

Quantifying winter survival of alfalfa [Medicago sativa (L.)]

Winter injury of alfalfa [Medicago sativa (L.)] in the northern United States decreases its economic and ecosystem benefits. Therefore, continued improvement in alfalfa cultivar winter survival (WS) is crucial for sustaining the productivity of this perennial crop. The North American Alfalfa Improvement Conference (NAAIC) standard test for WS recommends measuring the WS of spaced plants established in rows the previous spring. Measurement of WS of alfalfa grown in sward plots used by plant breeders would increase data collection and better reflect the potential for WS when grown in production fields. We conducted trials at seven location-year environments spanning from Wisconsin to South Dakota in the northern United States. These trials involved six check cultivars and followed protocols from the NAAIC standard test. The objectives were to determine (1) if WS and biomass yield assessment from sward plots were similar to those from the standard spaced planted row ratings and (2) if location-dependent environmental conditions affected the usefulness of alternative approaches for measuring WS. Estimation of WS using spaced plants and sward measurements was highly correlated, while correlations between the WS of the spaced planted rows and biomass yields were less. The number of locations required for spaced and sward plantings to determine cultivar differences was at least two, with four replications per location. Measuring WS from swards can enhance data collection and its relevance to on-farm alfalfa production, as sward plots serve a dual purpose by allowing both WS testing and evaluation of yield, making them a practical choice in comparison to the exclusive use of spaced plants in rows for WS testing. Availability of sward-plot WS descriptions of alfalfa cultivars will enhance decision making by producers

Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18

Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa (Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model’s overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL)

The low-virulent African swine fever virus (ASFV/NH/P68) induces enhanced expression and production of relevant regulatory cytokines (IFNα, TNFα and IL12p40) on porcine macrophages in comparison to the highly virulent ASFV/L60

The impact of infection by the low-virulent ASFV/NH/P68 (NHV) and the highly virulent ASFV/L60 (L60) isolates on porcine macrophages was assessed through the quantification of IFNα, TNFα, IL12p40, TGFβ and ASFV genes by real-time PCR at 2, 4 and 6 h post-infection. Increased IFNα, TNFα and IL12p40 expression was found in infection with NHV, in which expression of TGFβ was lower than in infection with L60. Principal component analysis showed a positive interaction of cytokines involved in cellular immune mechanisms, namely IFNα and IL12p40 in the NHV infection. Quantification by ELISA confirmed higher production of IFNα, TNFα and IL12p40 in the NHV-infected macrophages. Overall, our studies reinforce and clarify the effect of the NHV infection by targeting cellular and cellular-based immune responses relevant for pig survival against ASFV infection

Induction of neutralizing antibodies specific for the envelope proteins of the koala retrovirus by immunization with recombinant proteins or with DNA

Background: The koala retrovirus (KoRV) is the result of a transspecies transmission of a gammaretrovirus with fatal consequences for the new host. Like many retroviruses, KoRV induces lymphoma, leukemia and an immunodeficiency that is associated with opportunistic infections in the virus-infected animals. We recently reported the induction of neutralizing antibodies by immunization with the recombinant ectodomain of the transmembrane envelope protein p15E of KoRV. Since the neutralization titers of the p15E-specific sera were only moderate, we investigated the use of the surface envelope protein gp70 to induce neutralizing antibodies. Findings: We immunized rats and goats with the recombinant gp70 protein of the KoRV, an unglycosylated protein of 52kD (rgp70/p52) or with the corresponding DNA. In parallel we immunized with recombinant rp15E or with a combination of rp15E and rgp70/p52. In all cases binding and neutralizing antibodies were induced. The gp70-specific sera had titers of neutralizing antibodies that were 15-fold higher than the p15E-specific sera. Combining rp15E and rgp70/p52 did not significantly increase neutralizing titers compared to rgp70/p52 alone. High titers of neutralizing antibodies specific for gp70 were also induced by immunization with DNA. Since KoRV and PERV are closely related, we investigated cross-neutralization of the antisera. The antisera against p15E and gp70 of PERV and KoRV inhibited infection by both viruses. Conclusion: The envelope proteins of the KoRV may therefore form the basis of an effective preventive vaccine to protect uninfected koalas from infection and possibly an immunotherapeutic treatment for those already infected

Should Attractive Males Sneak: The Trade-Off between Current and Future Offspring

Peer reviewe