Breeding of a new variety of peanut with high-oleic-acid content and high-yield by marker-assisted backcrossing

Research and developmental strategies to hasten the improvement of orphan crops

To feed the world's expanding population, crop breeders need to increase agricultural productivity and expand major crops base. Orphan crops are indigenously important crops with great potential because they are climate resilient, highly nutritious, contain nutraceutical compounds, and can improve the livelihood of smallholder farmers and consumers, but they have received little or no scientific attention. This review article examines several research and developmental strategies for hastening the improvement of these crops so that they can effectively play their role in securing food and nutrition. The integration of both research and developmental approaches will open up modern opportunities for crop improvement. We summarized ways in which advanced tools in phenotyping and genotyping, using high-throughput processes, can be used to accelerate their improvement. Finally, we suggest roles the genebanks can play in improving orphan crops, as the utilization of plant genetic resources is important for the genetic improvement of a crop.

Development and validation of a rapid method for the determination of oil and oleic acid content in peanut (Arachis hypogaea L.) using time domain nuclear magnetic resonance

Peanuts (Arachis hypogaea L.) are an important nut product used in various cuisines worldwide. Many different analytical procedures are in place to ensure its nutritional properties including total oil content and oleic acid content which stand out among the most important parameters for assessing the nutritional quality of the kernels as well as their shelf-life. The analytical procedures for the determination of these two properties are extremely laborious and time-consuming. We developed and validated two methods using Time Domain-Nuclear Magnetic Resonance (TD-NMR) for the determination of these parameters in less than 5 min without any need for sample preparation. The methods were validated and tested with samples from different sources and profiles, ensuring fitness-for-purpose with good repeatability and trueness in all cases. The developed and validated method for determination of oleic acid fraction using multivariate calibration was embedded on a web-based application that allows easy use with very limited personnel training and a calibration transfer approach was developed to allow for this model to be used on a different instrument, therefore both methods are readily applicable for commercial and research peanut samples.

Metabolomic and Transcriptomic Analysis Reveals Flavonoid-Mediated Regulation of Seed Antioxidant Properties in Peanut Seed Vigor

Peanut (Arachis hypogaea L.) is an oilseed crop grown worldwide. Flavonoids have profound benefits for plant growth and development because of their powerful antioxidant properties. Seed vigor is an important indicator of seed quality. However, how flavonoids impact seed vigor formation in large-seed peanuts is still poorly understood. Here, we profiled flavonoids, phytohormones, and transcriptomes of developing seeds of large-seed peanut varieties with low (ZP06) and high (H8107) seed vigor. A total of 165 flavonoids were identified, 51 of which were differentially accumulated in ZP06 and H8107. Lower levels of dihydromyricetin (0.28 times) and hesperetin-7-O-glucoside (0.26 times) were observed in ZP06 seeds than in H8107. All flavonoid biosynthesis structural genes were down-regulated in ZP06. The different hormone levels found in ZP06 and H8107 seeds could be associated with the expression of flavonoid biosynthesis genes via MYB and bHLH transcription factors. Dihydromyricetin could relate to ZP06's poor seed vigor by impacting its seed antioxidant properties. Thus, the presence of flavonoids in large-seed peanuts could contribute to their physiological quality and germination potential through controlling the accumulation of reactive oxygen species to improve seed antioxidant properties.

MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing

Introduction

The emergence rate of crop seedlings is an important indicator for variety selection, evaluation, field management, and yield prediction. To address the low recognition accuracy caused by the uneven size and varying growth conditions of crop seedlings under salt-alkali stress, this research proposes a peanut seedling recognition model, MS-YOLOv8.

Methods

This research employs close-range remote sensing from unmanned aerial vehicles (UAVs) to rapidly recognize and count peanut seedlings. First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. Additionally, the module automatically adjusts the channel weights of each group based on their contribution, improving the feature fusion effect. Second, the neck network structure is reconstructed to enhance recognition capabilities for small objects, and the MPDIoU loss function is introduced to effectively optimize the detection boxes for seedlings with scattered branch growth.

Results

Experimental results demonstrate that the proposed MS-YOLOv8 model achieves an AP50 of 97.5% for peanut seedling detection, which is 12.9%, 9.8%, 4.7%, 5.0%, 11.2%, 5.0%, and 3.6% higher than Faster R-CNN, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and RT-DETR, respectively.

Discussion

This research provides valuable insights for crop recognition under extreme environmental stress and lays a theoretical foundation for the development of intelligent production equipment.

Marker-assisted introgression to improve the oleic acid content in the TMV 7 groundnut (Arachis hypogaea L.) variety suitable for the oil industry

BACKGROUND

Improving the quality and shelf life of groundnut oil is one of the foremost objectives of groundnut breeding programmes. This can be achieved by marker-assisted introgression, a technique that efficiently and precisely enables breeders to develop plants with enhanced qualities. This study focused on improving the oleic acid content of an elite groundnut variety, TMV 7, by introgressing a recessive mutation responsible for the increase in oleic acid from ICG 15419. Hybridization was performed between the donor and recurrent parents to develop the F1, BC1F1, BC2F1 and BC2F2 populations. Introgressed lines with increased oleic acid in the genetic background of TMV 7 were identified using allele-specific marker, F435-F, F435SUB-R and a set of SSR markers were employed to recover the genome of the recurrent parent.

RESULTS

With two backcrosses, a total of ten homozygous plants in the BC2F2 population were identified with oleic acid content ranging from 54.23 to 57.72% causing an increase of 36% over the recurrent parent. Among the ten lines, the line IL-23 exhibited the highest level of recurrent parent genome recovery of 91.12%.

CONCLUSIONS

The phenotypic evaluation of 10 homozygous introgressed lines indicated fewer differences for all other traits under study compared to the recurrent parent, except for oleic acid and linoleic acid content confirming the genetic background of the recurrent parent. The identified lines will be subjected to multilocation trials before their commercial release.

High-yield hybrid breeding of Camellia oleifolia based on ISSR molecular markers

BACKGROUND

C. Oleifera is among the world's largest four woody plants known for their edible oil production, yet the contribution rate of improved varieties is less than 20%. The species traditional breeding is lengthy cycle (20-30 years), occupation of land resources, high labor cost, and low accuracy and efficiency, which can be enhanced by molecular marker-assisted selection. However, the lack of high-quality molecular markers hinders the species genetic analysis and molecular breeding.

RESULTS

Through quantitative traits characterization, genetic diversity assessment, and association studies, we generated a selection population with wide genetic diversity, and identified five excellent high-yield parental combinations associated with four reliable high-yield ISSR markers. Early selection criteria were determined based on kernel fresh weight and cultivated 1-year seedling height, aided by the identification of these 4 ISSR markers. Specific assignment of selected individuals as paternal and maternal parents was made to capitalize on their unique attributes.

CONCLUSIONS

Our results indicated that molecular markers-assisted breeding can effectively shorten, enhance selection accuracy and efficiency and facilitate the development of a new breeding system for C. oleifera.

Improving linolenic acid content in rapeseed oil by overexpression of CsFAD2 and CsFAD3 genes

With the increasing public attention to the health benefit of polyunsaturated fatty acids (PUFAs) and demand for linolenic acid (C18:3), it is of great significance to increase the C18:3 content in our meal. As an oil crop with high content of C18:3, Camelina sativa has three homologous copies of FAD2 and three homologous copies FAD3. In this study, we seed-specifically overexpressed two Camelina sativa fatty acid desaturase genes, CsFAD2 and CsFAD3, in rapeseed cultivar Zhongshuang 9. The results show that C18:3 content in CsFAD2 and CsFAD3 overexpressed seeds is increased from 8.62% in wild-type (WT) to 10.62-12.95% and 14.54-26.16%, respectively. We crossed CsFAD2 and CsFAD3 overexpression lines, and stable homozygous digenic crossed lines were obtained. The C18:3 content was increased from 8.62% in WT to 28.46-53.57% in crossed overexpression lines. In addition, we found that the overexpression of CsFAD2 and CsFAD3 had no effect on rapeseed growth, development, and other agronomic traits. In conclusion, we successfully generated rapeseed germplasms with high C18:3 content by simultaneously overexpressing CsFAD2 and CsFAD3, which provides a feasible way for breeding high C18:3 rapeseed cultivars.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01445-0.

Identification of Putative Quantitative Trait Loci for Improved Seed Oil Quality in Peanuts

Improving seed oil quality in peanut (Arachis hypogaea) has long been an aim of breeding programs worldwide. The genetic resources to achieve this goal are limited. We used an advanced recombinant inbred line (RIL) population derived from JH5 × KX01-6 to explore quantitative trait loci (QTL) affecting peanut oil quality and their additive effects, epistatic effects, and QTL × environment interactions. Gas chromatography (GC) analysis suggested seven fatty acids components were obviously detected in both parents and analyzed in a follow-up QTL analysis. The major components, palmitic acid (C16:0), oleic acid (C18:1), and linoleic acid (C18:2), exhibited considerable phenotypic variation and fit the two major gene and minor gene mixed-inheritance model. Seventeen QTL explained 2.57-38.72% of the phenotypic variation in these major components, with LOD values of 4.12-37.56 in six environments, and thirty-five QTL explained 0.94-32.21% of the phenotypic variation, with LOD values of 5.99-150.38 in multiple environments. Sixteen of these QTL were detected in both individual and multiple environments. Among these, qFA_08_1 was a novel QTL with stable, valuable and major effect. Two other major-effect QTL, qFA_09_2 and qFA_19_3, share the same physical position as FAD2A and FAD2B, respectively. Eleven stable epistatic QTL involving nine loci explained 1.30-34.97% of the phenotypic variation, with epistatic effects ranging from 0.09 to 6.13. These QTL could be valuable for breeding varieties with improved oil quality.

An Overview of Mapping Quantitative Trait Loci in Peanut (Arachis hypogaea L.)

Quantitative Trait Loci (QTL) mapping has been thoroughly used in peanut genetics and breeding in spite of the narrow genetic diversity and the segmental tetraploid nature of the cultivated species. QTL mapping is helpful for identifying the genomic regions that contribute to traits, for estimating the extent of variation and the genetic action (i.e., additive, dominant, or epistatic) underlying this variation, and for pinpointing genetic correlations between traits. The aim of this paper is to review the recently published studies on QTL mapping with a particular emphasis on mapping populations used as well as traits related to kernel quality. We found that several populations have been used for QTL mapping including interspecific populations developed from crosses between synthetic tetraploids and elite varieties. Those populations allowed the broadening of the genetic base of cultivated peanut and helped with the mapping of QTL and identifying beneficial wild alleles for economically important traits. Furthermore, only a few studies reported QTL related to kernel quality. The main quality traits for which QTL have been mapped include oil and protein content as well as fatty acid compositions. QTL for other agronomic traits have also been reported. Among the 1261 QTL reported in this review, and extracted from the most relevant studies on QTL mapping in peanut, 413 (~33%) were related to kernel quality showing the importance of quality in peanut genetics and breeding. Exploiting the QTL information could accelerate breeding to develop highly nutritious superior cultivars in the face of climate change.

Peanut leaf disease identification with deep learning algorithms

Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%-23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases' detection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01370-8.