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Saponins are an important group found in Chenopodium quinoa. They represent an obstacle for the use of quinoa as food for humans and animal feeds because of their bitter taste and toxic effects, which necessitates their elimination. Several saponins elimination methods have been examined to leach the saponins from the quinoa seeds; the wet technique remains the most used at both laboratory and industrial levels. Dry methods (heat treatment, extrusion, roasting, or mechanical abrasion) and genetic methods have also been evaluated. The extraction of quinoa saponins can be carried out by several methods; conventional technologies such as maceration and Soxhlet are the most utilized methods. However, recent research has focused on technologies to improve the efficiency of extraction. At least 40 saponin structures from quinoa have been isolated in the past 30 years, the derived molecular entities essentially being phytolaccagenic, oleanolic and serjanic acids, hederagenin, 3β,23,30 trihydroxy olean-12-en-28-oic acid, 3β-hydroxy-27-oxo-olean-12en-28-oic acid, and 3β,23,30 trihydroxy olean-12-en-28-oic acid. These metabolites exhibit a wide range of biological activities, such as molluscicidal, antifungal, anti-inflammatory, hemolytic, and cytotoxic properties.

Tribolium castaneum can inflict significant harm to stored grains. The use of novel, harmless, and effective biopesticides is needful to avoid the hazardous effects of chemical insecticides. On the other hand, saponins are antinutritional metabolites with large biological properties, and quinoa husk is one of the richest biomasses in these compounds. The biocidal and antinutritional effects of a saponin-rich extract (SRE) from Chenopodium quinoa husk were examined against T. castaneum adults by evaluating mortality, nutritional indices, and mortality rate. The effect on T. castaneum ’s enzymatic activity was also investigated. As a result, at concentrations above 25 mg/g of SRE, insects can no longer consume flour with a significant feeding deterrent (≥ 84.20%). The study showed that SRE has an acute and long-term effect on insect survival, which confirms that the mortality of T. castaneum is attributable to the toxic action of the saponin extract and not just the starvation action. In addition, SRE demonstrated its capacity to disrupt the wax layer of T. castaneum adults, penetrate insects, and react as oxidative stressors, which explain the affectation of the immune defense system of T. castaneum through the downregulation of phenoloxidase activity and glutathione S-transferase, the upregulation of the antioxidant system presented in this study with catalase activity, and causing organelle damage in the midgut tissues confirmed by the inhibition of amylase activity. According to the findings of this study, saponin extract has a very interesting application as an insecticide against storage insects.

Quinoa is a resilient, nutrient-rich crop with strong potential for cultivation in marginal environments, yet it remains underutilized and under-researched, particularly in the context of automated yield estimation. In this study, we introduce a novel deep learning approach for quinoa panicle detection and counting using instance segmentation via Mask R-CNN, enhanced with an EfficientNet-B7 backbone and Mish activation function. We conducted a comparative analysis of various backbone architectures, and our improved model demonstrated superior performance in accurately detecting and segmenting individual panicles. This instance-level detection enables more precise yield estimation and offers a significant advancement over traditional methods. To the best of our knowledge, this is the first application of instance segmentation for quinoa panicle analysis, highlighting the potential of advanced deep learning techniques in agricultural monitoring and contributing valuable benchmarks for future AI-driven research in quinoa cultivation.

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Quinoa (Chenopodium quinoa Willd.) is a grain of great nutritional interest that gained international importance during the last decade. Before its consumption, this grain goes through many processes that can alter its nutritional value. Here we report the effect of processing (polishing and milling) and cooking (boiling and steaming) on the saponin content, mineral profile of 14 elements using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), protein content, and total phenolic compound. The polishing caused an average drop in the saponin content from 1.7% to 0.46% but induced important losses in mineral content (K, Mg, Ca, Zn, Co, Cu, Fe, Mn, and Ni), and phenolic compounds. However, the greatest nutritional degradation happened after milling due to the elimination of seed teguments and embryos, where over 50% of many minerals, 60% of protein content, and almost the totality of phenolic compounds, were lost. Cooking effect was less important than processing, but some significant losses were attested. Boiling caused a loss of up to 40% for some minerals like K, B, and Mo because of their hydrosolubility, and 88% of the polyphenols, while steaming allowed a better retention of those nutrients. Consuming polished quinoa instead of semolina and using steaming instead of boiling are trade-offs consumer needs to make to get optimal benefits from quinoa virtues.

[This corrects the article DOI: 10.3389/fpls.2025.1472688.].

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Sustainable food production requires global access to fertilizers, reducing yield gaps in marginal lands, and decarbonizing the agricultural sector. This study evaluates plant growth-promoting rhizobacteria (PGPRs) preserved in silk-trehalose seed coatings for six months under ambient conditions for their potential to enhance crop yields in challenging soils. Common bean seeds were coated with silk, trehalose, and Rhizobium tropici using a low-tech pan-coating method and tested in greenhouse experiments and field trials across three Moroccan experimental farms with contrasting soil types (favorable, low organic matter, and saline). Rhizosphere microbial communities were characterized using 16S rRNA gene sequencing, and grain nutritional quality was assessed by ICP-OES analysis. Coated seeds showed improved vigor, larger biomass, and enhanced root architecture compared to non-coated seeds under stress conditions. Field trials demonstrated that seed treatment was associated with 50–75% increases in yield parameters and a 53% increase in grain zinc concentration, depending on soil conditions. Additionally, the rhizosphere of treated plants exhibited an enhanced presence of beneficial microbes, such as Bacillus and Acidobacteria, without disrupting native bacterial communities. This low-tech seed coating approach offers a promising sustainable solution for enhancing food production and nutritional quality in resource-limited, environmentally challenged regions.

Seed priming has been for a long time an efficient application method of biofertilizers and biocontrol agents. Due to the quick degradation of the priming agents, this technique has been limited to specific immediate uses. With the increase of awareness of the importance of sustainable use of biofertilizers, seed coating has presented a competitive advantage regarding its ability to adhere easily to the seed, preserve the inoculant, and decompose in the soil. This study compared primed Phaseolus vulgaris seeds with Rhizobium tropici and trehalose with coated seeds using a silk solution mixed with R. tropici and trehalose. We represented the effect of priming and seed coating on seed germination and the development of seedlings by evaluating physiological and morphological parameters under different salinity levels (0, 20, 50, and 75 mM). Results showed that germination and morphological parameters have been significantly enhanced by applying R. tropici and trehalose. Seedlings of coated seeds show higher root density than the freshly primed seeds and the control. The physiological response has been evaluated through the stomatal conductance, the chlorophyll content, and the total phenolic compounds. The stability of these physiological traits indicated the role of trehalose in the protection of the photosystems of the plant under low and medium salinity levels. R. tropici and trehalose helped the plant mitigate the negative impact of salt stress on all traits. These findings represent an essential contribution to our understanding of stress responses in coated and primed seeds. This knowledge is essential to the design of coating materials optimized for stressed environments. However, further progress in this area of research must anticipate the development of coatings adapted to different stresses using micro and macro elements, bacteria, and fungi with a significant focus on biopolymers for sustainable agriculture and soil microbiome preservation.