Improvement of Cercospora leaf spot and powdery mildew resistance of mungbean variety KING through marker-assisted selection

The development of resistant mungbean varieties is one of the most efficient strategies to control major diseases such as Cercospora leaf spot (CLS) and powdery mildew (PM). The objectives of this study were to pyramid a CLS resistance gene and two PM resistance genes from the donor parent D2 into a susceptible variety KING through marker-assisted backcrossing (MABC) and to evaluate their agronomic traits and disease resistance under field conditions. Five markers linked to the resistance genes were used for foreground selection, while two marker sets [Set A containing 15 polymorphic simple sequence repeat (SSR) and expressed sequence tag-SSR (EST-SSR) markers and Set B containing 34 polymorphic inter-simple sequence repeat (ISSR) loci] were also used for background selection. Two pyramided backcross (BC) lines, namely H3 and H4, were homozygous at all five marker loci when confirmed in BC4F4 and BC4F5 generations. Their recurrent parent genome (RPG) recovery ranged from 96.4 to 100.0%, depending on the marker sets. During field evaluation, a moderate to high level of CLS and PM resistance was observed in both BC lines compared to the susceptible recurrent parent KING. One of these BC lines (H3) had all agronomic traits similar or superior to the recurrent parent KING at all environments, and had a higher yield than KING (18.0–32.0%) under CLS and PM outbreaks. This line can be developed into a new resistant mungbean variety in Thailand in the future. These results substantiate the usefulness of MABC for transferring multiple resistance genes into an elite variety.

Validation of molecular markers linked to Cercospora leaf spot disease resistance in mungbean (Vigna radiata [L.] Wilczek)

Cercospora leaf spot (CLS) resistance is a highly desirable trait for mungbean (Vigna radiata [L.] Wilczek) production in Thailand. ‘V4718’ is a vital resistance source that shows high and stable resistance to CLS disease. A previous study identified a major quantitative trait locus (QTL) (qCLSC72V18-1) controlling CLS resistance and found the marker (I16274) that was located closest to the resistance gene by using F2:9 and F2:10 recombinant inbred line populations derived through a cross between ‘V4718’ and the susceptible variety ‘Chai Nat 72’ (‘CN72’). Here, we evaluated three newly reported simple sequence repeat (SSR) markers and one InDel marker together with six previously identified markers that were linked to qCLSC72V18-1 to further identify the markers that were located close to this QTL. By performing bulk segregant analysis on two validation populations, we found that two SSR markers (Vr6gCLS037 and Vr6gCLS133) and one InDel marker (VrTAF5_indel) were putatively associated with CLS resistance. Of these markers, only the VrTAF5_indel marker showed a significant association with the CLS resistance gene with a logarithm of odds score > 3 across the phenotypic data for 2016 and 2018. QTL analysis with inclusive composite interval mapping revealed that the VrTAF5_indel marker was integrated into the genetic map with other previously identified markers. The I16274 and VrTAF5_indel markers flanking the QTL of interest accounted for 41.56%-60.38% of the phenotypic variation with genetic distances of 4.0 and 5.0 cM from the resistance gene, respectively. Both markers together permitted only 0.40% recombination with the CLS resistance gene in marker-assisted selection and thus could be useful in future breeding efforts for CLS resistance in mungbean.

Seed-Borne Cercospora beticola can Initiate Cercospora Leaf Spot from Sugar Beet (Beta vulgaris L.) Fruit Tissue

Cercospora leaf spot (CLS) is a globally important disease of sugar beet (Beta vulgaris L.) caused by the fungus Cercospora beticola. Long-distance movement of C. beticola has been indirectly evidenced in recent population genetic studies, suggesting potential dispersal via seed. Commercial sugar beet “seed” consists of the reproductive fruit (true seed surrounded by maternal pericarp tissue) coated in artificial pellet material. In this study, we confirmed the presence of viable C. beticola in sugar beet fruit for 10 of 37 tested seed lots. All isolates harbored the G143A mutation associated with quinone outside inhibitor resistance and 32 of 38 isolates had reduced demethylation inhibitor sensitivity (EC50 > 1 µg/ml). Planting of commercial sugar beet seed demonstrated the ability of seed-borne inoculum to initiate CLS in sugar beet. Cercospora beticola DNA was detected in DNA isolated from xylem sap, suggesting the vascular system is used to systemically colonize the host. We established nuclear ribosomal internal transcribed spacer region amplicon sequencing using the MinION platform to detect fungi in sugar beet fruit. Fungi from 19 different genera were identified from 11 different sugar beet seed lots, but Fusarium, Alternaria, and Cercospora were consistently the three most dominant taxa, comprising an average of 93% relative read abundance over 11 seed lots. We also present evidence that C. beticola resides in the pericarp of sugar beet fruit, rather than the true seed. The presence of seed-borne inoculum should be considered when implementing integrated disease management strategies for CLS of sugar beet in the future.

Bacterial Endophytes As Indicators of Susceptibility To Cercospora Leaf Spot (CLS) Disease In Beta vulgaris L.

Background: The fungus Cercospora beticola causes Cercospora Leaf Spot (CLS) of sugar beet (Beta vulgaris L.). Despite the global importance of this disease, durable resistance to CLS has still not been obtained. Therefore, the development of tolerant hybrids is still a major goal for sugar beet breeding. Although recent studies have suggested that the leaf microbiome composition can offer useful predictors to assist plant breeders, this is an untapped resource in sugar beet breeding efforts. Methods: Using Ion GeneStudio S5 technology to sequence amplicons from seven 16S rRNA hypervariable regions, the most recurring endophytes discriminating CLS-symptomatic and symptomless sea beets (Beta vulgaris L.ssp. maritima) were identified. This allowed the design of taxon-specific primer pairs to quantify the abundance of the most representative endophytic species in large naturally occurring populations of sea beet and subsequently in sugar beet breeding genotypes under either CLS symptomless or infection stages using qPCR. Results: Among the screened bacterial genera, Methylobacterium and Mucilaginibacter were found to be significantly (p<0.05) more abundant in symptomatic sea beets with respect to symptomless. In cultivated sugar beet material under CLS infection, the comparison between resistant and susceptible genotypes confirmed that the susceptible genotypes hosted higher contents of the above-mentioned bacterial genera. Conclusions: These results suggest that the abundance of these species can be correlated with increased sensitivity to CLS disease. This evidence can further prompt novel protocols to assist plant breeding of sugar beet in the pursuit of improved pathogen resistance.

Efficacy of Selected Bio-Agents and Neem Cake on Cercospora Leaf Spot and Growth of Black Gram (Vigna mungo L.)

Black gram (Vigna mungo L.) is a vital pulse crop globally and one of the most vital pulse in India. It is understood to be affected by many varieties of diseases, Cercospora leaf spot is certainly considered one among them. Cercospora leaf spot due to Cercospora canescens causes much damage to the production of black gram. The neem cake, Trichoderma viride, Pseudomonas fluorescens, Paecilomyces lilacinus, Carbendazim were tested under field conditions during Rabi 2020-2021 for their efficacy against the disease and growth and yield parameters. A survey was conducted during Rabi, 2020-2021 to know the severity of Cercospora leaf spot of black gram in farmer’s fields in Kurnool district of Andhra Pradesh. In-situ (field) experiment was carried out in randomized block design with five treatments and three replications. The highest plant height at 60 DAS (56.96 cm), fresh weight (35.59 gm), dry weight (14.98 gm), number of pods per plant (18.17 pods/plant), yield (7.96 q/ha) and Benefit Cost ratio (1:3.48) showing better result when treated with treatment neem cake @ 0.5 t/ha + Trichoderma viride @ 2.5 kg/ha. The treatment T1 – neem cake @ 0.5 t/ha + Trichoderma viride @ 2.5 kg/ha significantly decreased the disease intensity at 30, 45 and 60 DAS (10.02%), (12.02%) and (16.42%) respectively. It is concluded that T1 – neem cake @ 0.5 t/ha + Trichoderma viride @ 2.5 kg/ha found superior in all the growth and yield parameters.

Crop identification and disease classification using traditional machine learning and deep learning approaches

Crop and disease classification is one of the important problems in automation of agricultural processes with multi-cropping method where the field is cultivated with more than one crop. In order to solve this classification problem, a study has been carried out in the field cultivating eggplant (Solanum melongena) and tomato (Solanum lycopersicum) using the images obtained from a mobile phone camera. Textural descriptors namely contrast, correlation, energy and homogeneity were extracted from the gray-scale converted RGB image for crop identification, i.e., (tomato or eggplant) and the same descriptors were extracted from the gray-scale converted image from Hue Saturation Value (HSV) for disease classification (due to Cercospora leaf spot disease or two-spotted spider infestation). Discriminant analysis, Naive Bayes algorithm, support vector machine and neural network were the classification algorithms used with a resulting best accuracy of 97.61%, 95.62%, 98.01% and 98.94% for crop identification, 86.09%, 76.52%, 86.96% and 86.04% for disease classification respectively. Similarly, application of algorithm with 6 histogram-based descriptors for health status detection resulted in an accuracy of 66.67%, 37.04%, 50% and 72.9% respectively. Deep learning algorithm namely AlexNet was also evaluated which resulted in an accuracy of 100% for crop identification, 89.36% for health status detection and 81.51% for disease classification. Among the algorithms, AlexNet resulted in the best average accuracy of 90.29% for the above classification tasks.