Improvement of plant disease classification accuracy with generative model-synthesized training datasets

Digitalization in agriculture requires critical research into applications of artificial intelligence to various specialization domains. This work aimed at investigating the application of image synthesis technology to the mitigation of the data volume constraint to digital plant disease phenotyping accuracy. We designed an experiment involving the use of a deep convolutional generative adversarial network (DC-GAN) to synthesize photorealistic data for healthy and bacterial spot disease-infected tomato leaves. The training dataset contained 1,272 instances per class. We further employed a 3-block visual geometry group (VGG) convolutional neural network (CNN) model with dropout regularization and 1 epoch to compare classification accuracies of the original dataset and various synthetic datasets. Our results showed that the third DC-GAN synthesized training dataset containing 3,816 synthetic examples of both healthy and bacterial spot infected tomato leaf classes outperformed the original training dataset containing 1,272 real examples of both tomato leaf classes (77.088% accuracy with the former dataset on a 3-block VGG CNN model with dropout regularization and 1 epoch, as compared to 76.447% accuracy with the latter dataset on the same classifier)

Application of Principal Component Analysis to advancing digital phenotyping of plant disease in the context of limited memory for training data storage

Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase

Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection

The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using a combination of genetic markers and advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, machine learning algorithms and high-performance computing was employed. First, genetic markers highly associated with disease resistance were identified using next-generation sequencing data and statistical analysis. Then, an adiabatic quantum machine learning algorithm was developed to integrate these markers into a single predictor of disease susceptibility. The results demonstrate that the integrative use of genetic markers and adiabatic quantum machine learning significantly improved the accuracy and efficiency of disease resistance-based marker-assisted plant selection. By leveraging the power of adiabatic quantum computing and genetic markers, more effective and efficient strategies for disease resistance-based marker-assisted plant selection can be developed

Genomic selection in tropical perennial crops and plantation trees: a review

International audienceTo overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities

Genetic determinism of oil acidity among some DELI oil palm (Elaeis guineensis Jacq.) progenies

The oil palm (Elaeis guineensis Jacq.) is cultivated mainly for crude palm oil (CPO) which is extracted from the mesocarp of fruits. The quality of CPO is generally impaired due to high acidity, as a result of the activity of a lipase present in the mesocarp of the fruits at maturity. The objective of this study was to establish the genetic determinism of “palm oil acidity” (POA) from E. guineensis. Acidity was analyzed on CPO from the mesocarp of ripe fruits of some DELI parent palms used for the production of commercial seeds at CEREPAH Dibamba. Acidity analysis of 457 individuals from 11 progenies, issued from nine parents showed that, the segregation of forms with respect to this trait is compatible with a monohybridism with dominance. The dominant allele denoted that “Pa” determines high acidity while the recessive allele “pa” favours production of oil with low acidity.Keywords: Elaeis guineensis Jacq., free fatty acid content, crude palm oil, inheritanc

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

International audienceGenomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas ( Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought—two major threats to banana production—used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome

Optimizing imputation of marker data from genotyping-by-sequencing (GBS) for genomic selection in non-model species: Rubber tree (Hevea brasiliensis) as a case study

International audienceGenotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52

Response of cassava cultivars to African cassava mosaic virus infection across a range of inoculum doses and plant ages.

Cassava production in Africa is constrained by cassava mosaic disease (CMD) that is caused by the Cassava mosaic virus (CMV). The aim of this study was to evaluate the responses of a range of commonly cultivated West African cassava cultivars to varying inoculum doses of African cassava mosaic virus (ACMV). We grafted 10 cultivars of cassava plants with different inoculum doses of CMV (namely two, four, or six CMD-infected buds) when the experimental plants were 8, 10, or 12 weeks old, using non-inoculated plants as controls. Three cultivars showed disease symptoms when grafted with two buds, and four cultivars showed disease symptoms when grafted with four or six buds. Most cultivars became symptomatic six weeks after inoculation, but one ('TMS92/0326') was symptomatic two weeks after inoculation, and two ('Ntollo' and 'Excel') were symptomatic after four weeks. Root weight tended to be lower in the six-bud than in the two-bud dose, and disease severity varied with plant age at inoculation. These results indicate that the level of CMD resistance in cassava cultivars varies with inoculum dose and timing of infection. This will allow appropriate cultivars to be deployed in each production zone of Africa in accordance with the prevalence of CMD

Analysis of genetic diversity and agronomic variation in banana sub-populations for genomic selection under drought stress in southern Benin

International audienceIn the perspective of investigating genomic selection (GS) among Musa genotypes in West and Central Africa, banana accessions were phenotyped under natural drought stress in Benin and genotyped using genotyping by sequencing. Sixty-one (61) accessions grouped into three major genomic groups AAA, AAB and ABB and those without genomic affiliation information were used. Variation within the population was determined by phenotypic variables while population structure and clustering analysis were carried out to understand the ge-netic diversity at the molecular level. Among the genomic groups evaluated, the group AAB showed the best performance for fruit weight at maturity, (3.41 +/- 1.99 kg) and for plant height (198.46 +/- 12.66 cm). At the accession level, HD 117 S1 and NIA 27 showed the best plant height (263.16 +/- 20.98 cm) and the best fruit weight at maturity (9.43 +/- 0.0 kg) respectively. Phenotypic data did not reveal clear genetic diversity among accessions; however, the genetic diversity was conspicuous at the molecular level using 5000 markers. The af-filiations of local accessions in genomic groups were determined for the first time based on the phenotypic and molecular data obtained in this study. The knowledge generated allows the possibility to apply GS in banana

Screening of Triploid Banana Population Under Natural and Controlled Black Sigatoka Disease for Genomic Selection

International audienceBlack sigatoka disease (BSD) is the most important foliar threat in banana production, and breeding efforts against it should take advantage of genomic selection (GS), which has become one of the most explored tools to increase genetic gain, save time, and reduce selection costs. To evaluate the potential of GS in banana for BSD, 210 triploid accessions were obtained from the African Banana and Plantain Research Center to constitute a training population. The variability in the population was assessed at the phenotypic level using BSD- and agronomic-related traits and at the molecular level using single-nucleotide polymorphisms (SNPs). The analysis of variance showed a significant difference between accessions for almost all traits measured, although at the genomic group level, there was no significant difference for BSD-related traits. The index of non-spotted leaves among accessions ranged from 0.11 to 0.8. The accessions screening in controlled conditions confirmed the susceptibility of all genomic groups to BSD. The principal components analysis with phenotypic data revealed no clear diversity partition of the population. However, the structure analysis and the hierarchical clustering analysis with SNPs grouped the population into four clusters and two subpopulations, respectively. The field and laboratory screening of the banana GS training population confirmed that all genomic groups are susceptible to BSD but did not reveal any genetic structure, whereas SNP markers exhibited clear genetic structure and provided useful information in the perspective of applying GS