Deep learning: as the new frontier in high-throughput plant phenotyping

Arya, Sunny; Sandhu, Karansher S.; Singh, Jagmohan; Kumar, Sudhir

doi:10.1007/s10681-022-02992-3

Cited by 56 publications

(38 citation statements)

References 111 publications

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“…The PCR (Polymerase Chain Reaction)-based molecular markers, viz., SSRs or microsatellites, have been widely used for genetic mapping studies in horticultural perennials [ 23 , 24 , 25 ]. SSRs are co-dominant markers and have gained more importance in plant genetics studies due to their properties of higher polymorphism, abundance, multi-allelic nature, and extensive genome coverage [ 26 , 27 , 28 , 29 ]. Previously, SSR markers have also been exploited for molecular discrimination of guava germplasm [ 9 , 16 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Construction of Genetic Linkage Map and Mapping QTL Specific to Leaf Anthocyanin Colouration in Mapping Population ‘Allahabad Safeda’ × ‘Purple Guava (Local)’ of Guava (Psidium guajava L.)

Sohi

Gill

Chhuneja

et al. 2022

Plants

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In the present investigation, F1 hybrids were developed in guava (Psidium guajava L.) by crossing high leaf-anthocyanin reflective-index (ARI1) content cultivars purple guava (local) ‘PG’, ‘CISH G-1’ and low leaf-ARI1 content cultivar Seedless ‘SL’ with Allahabad Safeda ‘AS’. On the basis of phenotypic observations, high ARI1 content was observed in the cross ‘AS’ × ‘PG’ (0.214). Further, an SSR-markers-based genetic linkage map was developed from a mapping population of 238 F1 individuals derived from cross ‘AS’ × ‘PG’. The linkage map comprised 11 linkage groups (LGs), spanning 1601.7 cM with an average marker interval distance of 29.61 cM between adjacent markers. Five anthocyanin-content related gene-specific markers from apple were tested for parental polymorphism in the genotypes ‘AS’ and ‘PG’. Subsequently, a marker, viz., ‘MdMYB10F1′, revealed a strong association with leaf anthocyanin content in the guava mapping population. QTL (qARI-6-1) on LG6 explains much of the variation (PVE = 11.51% with LOD = 4.67) in levels of leaf anthocyanin colouration. This is the first report of amplification/utilization of apple anthocyanin-related genes in guava. The genotypic data generated from the genetic map can be further exploited in future for the enrichment of linkage maps and for identification of complex quantitative trait loci (QTLs) governing economically important fruit quality traits in guava.

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Section: Introductionmentioning

confidence: 99%

Construction of Genetic Linkage Map and Mapping QTL Specific to Leaf Anthocyanin Colouration in Mapping Population ‘Allahabad Safeda’ × ‘Purple Guava (Local)’ of Guava (Psidium guajava L.)

Sohi

Gill

Chhuneja

et al. 2022

Plants

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“…X-ray CT has also been used to study quality traits and plant morphology. Various other tools are available to study different biotic stresses in crop plants, including light detection and ranging, ight time, positron emission tomography, radio detection and ranging, sound detection and ranging, and magnetic resonance imaging (see Arya et al 2022, Gill et al 2022).…”

Section: X-ray Computed Tomographymentioning

confidence: 99%

“…Colored or RGB cameras are used to take real-time images of an object, equivalent to human perception. This is one of the cheapest and most used imaging techniques to provide phenotyping data for phenomics-assisted selection (Arya et al 2022). RGB imaging techniques provide three important bands (red, green, and blue) of the electromagnetic spectrum, which can be used to select and classify biotic stresses in crop plants.…”

Section: Visible Imagingmentioning

confidence: 99%

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Bisht

Saini

Kaur

et al. 2022

Preprint

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Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, and fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker trait associations involved in regulating biotic stress responses of soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We recommend using ‘integrated omics’ to understand how soybean responds to different biotic stresses. We discuss the potential challenges of integrating multi-omics for functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

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“…Facts show that compared with the machine learning methods, deep learning methods have better effects in the fields of image recognition and classification and have expanded to almost all the areas of plant phenotyping (Arya et al, 2022). This is mainly because deep learning can more easily perform high-precision data analysis on a large number of images (Gill et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

Shi

et al. 2022

Front. Plant Sci.

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Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.

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Deep learning: as the new frontier in high-throughput plant phenotyping

Cited by 56 publications

References 111 publications

Construction of Genetic Linkage Map and Mapping QTL Specific to Leaf Anthocyanin Colouration in Mapping Population ‘Allahabad Safeda’ × ‘Purple Guava (Local)’ of Guava (Psidium guajava L.)

Construction of Genetic Linkage Map and Mapping QTL Specific to Leaf Anthocyanin Colouration in Mapping Population ‘Allahabad Safeda’ × ‘Purple Guava (Local)’ of Guava (Psidium guajava L.)

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

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