Genetic diversity in cultivated carioca common beans based on molecular marker analysis

A wide array of molecular markers has been used to investigate the genetic diversity among common bean species. However, the best combination of markers for studying such diversity among common bean cultivars has yet to be determined. Few reports have examined the genetic diversity of the carioca bean, commercially one of the most important common beans in Brazil. In this study, we examined the usefulness of two molecular marker systems (simple sequence repeats – SSRs and amplified fragment length polymorphisms – AFLPs) for assessing the genetic diversity of carioca beans. The amount of information provided by Roger’s modified genetic distance was used to analyze SSR data and Jaccards similarity coefficient was used for AFLP data. Seventy SSRs were polymorphic and 20 AFLP primer combinations produced 635 polymorphic bands. Molecular analysis showed that carioca genotypes were quite diverse. AFLPs revealed greater genetic differentiation and variation within the carioca genotypes (Gst = 98% and Fst = 0.83, respectively) than SSRs and provided better resolution for clustering the carioca genotypes. SSRs and AFLPs were both suitable for assessing the genetic diversity of Brazilian carioca genotypes since the number of markers used in each system provided a low coefficient of variation. However, fingerprint profiles were generated faster with AFLPs, making them a better choice for assessing genetic diversity in the carioca germplasm

Deep learning powered real-time identification of insects using citizen science data

Insect-pests significantly impact global agricultural productivity and quality. Effective management involves identifying the full insect community, including beneficial insects and harmful pests, to develop and implement integrated pest management strategies. Automated identification of insects under real-world conditions presents several challenges, including differentiating similar-looking species, intra-species dissimilarity and inter-species similarity, several life cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. A deep-learning model, InsectNet, is proposed to address these challenges. InsectNet is endowed with five key features: (a) utilization of a large dataset of insect images collected through citizen science; (b) label-free self-supervised learning for large models; (c) improving prediction accuracy for species with a small sample size; (d) enhancing model trustworthiness; and (e) democratizing access through streamlined MLOps. This approach allows accurate identification (>96% accuracy) of over 2500 insect species, including pollinator (e.g., butterflies, bees), parasitoid (e.g., some wasps and flies), predator species (e.g., lady beetles, mantises, dragonflies) and harmful pest species (e.g., armyworms, cutworms, grasshoppers, stink bugs). InsectNet can identify invasive species, provide fine-grained insect species identification, and work effectively in challenging backgrounds. It also can abstain from making predictions when uncertain, facilitating seamless human intervention and making it a practical and trustworthy tool. InsectNet can guide citizen science data collection, especially for invasive species where early detection is crucial. Similar approaches may transform other agricultural challenges like disease detection and underscore the importance of data collection, particularly through citizen science efforts..This is a pre-print of the article Chiranjeevi, Shivani, Mojdeh Sadaati, Zi K. Deng, Jayanth Koushik, Talukder Z. Jubery, Daren Mueller, Matthew EO Neal et al. "Deep learning powered real-time identification of insects using citizen science data." arXiv preprint arXiv:2306.02507 (2023). DOI: 10.48550/arXiv.2306.02507. Copyright 2023 The Authors. Attribution 4.0 International (CC BY 4.0) Posted with permission

Selection of common bean lines with high grain yield and high grain calcium and iron concentrations

Genetic improvement of common bean nutritional quality has advantages in marketing and can contribute to society as a food source. The objective of this study was to evaluate the genetic variability for grain yield, calcium and iron concentrations in grains of inbred common bean lines obtained by different breeding methods. For this, 136 F7 inbred lines were obtained using the Pedigree method and 136 F7 inbred lines were obtained using the Single-Seed Descent (SSD) method. The lines showed genetic variability for grain yield, and concentrations of calcium and iron independently of the method of advancing segregating populations. The Pedigree method allows obtaining a greater number of lines with high grain yield. Selection using the SSD method allows the identification of a larger number of lines with high concentrations of calcium and iron in grains. Weak negative correlations were found between grain yield and calcium concentration (r = -0.0994) and grain yield and iron concentration (r = -0.3926). Several lines show genetic superiority for grain yield and concentrations of calcium and iron in grains and their selection can result in new common bean cultivars with high nutritional quality

Assessing quantitative resistance against Leptosphaeria maculans (phoma stem canker) in Brassica napus (oilseed rapte) in young plants

Quantitative resistance against Leptosphaeria maculans in Brassica napus is difficult to assess in young plants due to the long period of symptomless growth of the pathogen from the appearance of leaf lesions to the appearance of canker symptoms on the stem. By using doubled haploid (DH) lines A30 (susceptible) and C119 (with quantitative resistance), quantitative resistance against L. maculans was assessed in young plants in controlled environments at two stages: stage 1, growth of the pathogen along leaf veins/petioles towards the stem by leaf lamina inoculation; stage 2, growth in stem tissues to produce stem canker symptoms by leaf petiole inoculation. Two types of inoculum (ascospores; conidia) and three assessment methods (extent of visible necrosis; symptomless pathogen growth visualised using the GFP reporter gene; amount of pathogen DNA quantified by PCR) were used. In stage 1 assessments, significant differences were observed between lines A30 and C119 in area of leaf lesions, distance grown along veins/petioles assessed by visible necrosis or by viewing GFP and amount of L. maculans DNA in leaf petioles. In stage 2 assessments, significant differences were observed between lines A30 and C119 in severity of stem canker and amount of L. maculans DNA in stem tissues. GFP-labelled L. maculans spread more quickly from the stem cortex to the stem pith in A30 than in C119. Stem canker symptoms were produced more rapidly by using ascospore inoculum than by using conidial inoculum. These results suggest that quantitative resistance against L. maculans in B. napus can be assessed in young plants in controlled conditions. Development of methods to phenotype quantitative resistance against plant pathogens in young plants in controlled environments will help identification of stable quantitative resistance for control of crop diseases