Humic Acid-Functionalized Lignin-Based Coatings Regulate Nutrient Release and Promote Wheat Productivity and Grain Quality.

The rational application of fertilizers is crucial for achieving high crop yields and ensuring global food security. The use of biopolymers for slow-release fertilizers (SRFs) development has emerged as a game-changer and environmentally sustainable pathway to enhance crop yields by optimizing plant growth phases. Herein, with a renewed focus on circular bioeconomy, a novel functionalized lignin-based coating material (FLGe) was developed for the sustained release of nutrients. This innovative approach involved the extraction and sustainable functionalization of lignin through a solvent-free esterification reaction with humic acid─an organic compound widely recognized for its biostimulant properties in agriculture. The primary objective was to fortify the hydration barrier of lignin by reducing the number of its free hydroxyl groups, thereby enhancing release control, while simultaneously harnessing the agronomic benefits offered by humic acid. After confirming the synthesis of functionalized lignin (FLGe) through 13C NMR analysis, it was integrated at varying proportions into either a cellulosic or starch matrix. This resulted in the creation of five distinct formulations, which were then utilized as coatings for diammonium phosphate (DAP) fertilizer. Experimental findings revealed an improved morphology and hardness (almost 3-fold) of DAP fertilizer granules after coating along with a positive impact on the soil's water retention capacity (7%). Nutrient leaching in soil was monitored for 100 days and a substantial reduction of nutrients leaching up to 80% was successfully achieved using coated DAP fertilizer. Furthermore, to get a fuller picture of their efficiency, a pot trial was performed using two different soil textures and demonstrated that the application of FLGe-based SRFs significantly enhanced the physiological and agronomic parameters of wheat, including leaf evolution and root architecture, resulting in an almost 50% increase in grain yield and improved quality. The results proved the potential of lignin functionalization to advance agricultural sustainability and foster a robust bioeconomy aligning with the premise "from the soil to the soil".

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

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.

Field phenotyping for African crops: overview and perspectives.

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.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Methylcellulose/lignin biocomposite as an eco-friendly and multifunctional coating material for slow-release fertilizers: Effect on nutrients management and wheat growth.

To obviate adverse effects from the non-biodegradability of certain polymer-based slow-release fertilizers (SRFs) and to offset higher operational costs, the use of biopolymers as coating material has recently caught interest in the research circles. The present work aims to design a sustainable coating material based on biodegradable polymers. To this end, Alfa plant was initially exploited as a viable sustainable source for the extraction of lignin (LGe), which was in turn integrated into the development of a three-dimensional cross-linked network, including methylcellulose (MC) as a matrix and citric acid (CA) as a cross-linking agent. Then, the designed coating material was applied onto Di-ammonium Phosphate (DAP) and Triple Superphosphate (TSP) water-soluble fertilizers in a rotating pan machine. Chemical, physical, and biodegradation studies have confirmed that the coating material is environmentally-friendly. Nutrients release experiments in water as well as in soil environments have proved the effectiveness of the MC and MC/LGe coating layers in delaying the nutrients discharge. Besides, the nutrients release from coated DAP and TSP lasted longer than 30 days. Furthermore, the coating film enhanced the fertilizers mechanical resistance and boosted the soil water retention capacity. The agronomic evaluation has also confirmed their remarkable potential in enhancing wheat leaf area, chlorophyll content and biomass, in addition to the roots architecture and the final fruiting efficiency. These results showed that this hybrid composite could be used as an efficient coating material to produce slow-release fertilizers with multifunctional performances.

Box-Behnken Design: Wet Process Optimization for Saponins Removal From Chenopodium quinoa Seeds and the Study of Its Effect on Nutritional Properties.

The pseudocereal grain, Quinoa (Chenopodium quinoa Willd.), has a great nutritional value due to its high contents of proteins, fiber, minerals, and vitamins. However, saponins naturally present outside the grains represent an obstacle to their consumption as human food. Before consumption, the grains are subjected to various treatments, which alter their nutritional value. In an attempt to eliminate the maximum of saponins using the wet process, while minimizing the washing conditions and preserving the nutritional quality, we explored the effects of several parameters, including volume of water, treatment time, soaking time, number of washing, and water temperature, followed by an optimization process using Box-Behnken Design, and finally, the impact of this process on the physicochemical and techno-functional properties of six quinoa genotypes seeds was evaluated. As a result, the variation of the treatment time, volume, and temperature of the water positively affected the saponins leaching. According to the quadratic model, the maximum percentages of eliminated saponins (96.53%-96.77%) were found at a temperature of 50°C, treatment times from 60 to 69 min, and water volumes from 6.99 to 7.50 mL per gram of seeds. The optimized method did not affect the proteins and microelements content (Zn, Mn, B, Mo), while a slight decrease of macro-elements (K, P, Ca, S, Mg) was noted in the level of some genotypes. On the other hand, a significant improvement of the techno-functional properties such as water and oil holding capacity was noted, with a sharp drop-in emulsifying activity in all genotypes without affecting the standard values of pH (6.4-6.8) and moisture content (10%-11%) of the seeds. Hence, the optimized method showed to be a more potential method for saponins removal than the currently used dry method.

Bioformulation of Silk-Based Coating to Preserve and Deliver Rhizobium tropici to Phaseolus vulgaris Under Saline Environments.

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.

Engineering the Plant Microenvironment To Facilitate Plant-Growth-Promoting Microbe Association.

New technologies that enhance soil biodiversity and minimize the use of scarce resources while boosting crop production are highly sought to mitigate the increasing threats that climate change, population growth, and desertification pose on the food infrastructure. In particular, solutions based on plant-growth-promoting bacteria (PGPB) bring merits of self-replication, low environmental impact, tolerance to biotic and abiotic stressors, and reduction of inputs, such as fertilizers. However, challenges in facilitating PGPB delivery in the soil still persist and include survival to desiccation, precise delivery, programmable resuscitation, competition with the indigenous rhizosphere, and soil structure. These factors play a critical role in microbial root association and development of a beneficial plant microbiome. Engineering the seed microenvironment with protein and polysaccharides is one proposed way to deliver PGPB precisely and effectively in the seed spermosphere. In this review, we will cover new advancements in the precise and scalable delivery of microbial inoculants, also highlighting the latest development of multifunctional rhizobacteria solutions that have beneficial impact on not only legumes but also cereals. To conclude, we will discuss the role that legislators and policymakers play in promoting the adoption of new technologies that can enhance the sustainability of crop production.

Programmable design of seed coating function induces water-stress tolerance in semi-arid regions.

In semi-arid regions, water stress during seed germination and early seedling growth is the highest cause of crop loss. In nature, some seeds (for example, chia and basil) produce a mucilage-based hydrogel that creates a germination-promoting microenvironment by retaining water, regulating nutrient entry and facilitating interactions with beneficial microorganisms. Inspired by this strategy, a two-layered biopolymer-based seed coating has been developed to increase germination and water-stress tolerance in semi-arid, sandy soils. Seeds are coated with a silk/trehalose inner layer containing rhizobacteria and a pectin/carboxymethylcellulose outer layer that reswells upon sowing and acts as a water jacket. Using Phaseolus vulgaris (common bean) cultured under water-stress conditions in an experimental farm in Ben Guerir, Morocco, the proposed seed coating effectively delivered rhizobacteria to form root nodules, resulted in plants with better health and mitigated water stress in drought-prone marginal lands. A programmable seed coating technology has the potential to increase seed germination and water-stress tolerance in semi-arid, sandy soils.

Variations of Saponins, Minerals and Total Phenolic Compounds Due to Processing and Cooking of Quinoa (Chenopodium quinoa Willd.) Seeds.

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.

An Insight into Saponins from Quinoa (Chenopodium quinoa Willd): A Review.

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, 3ß,23,30 trihydroxy olean-12-en-28-oic acid, 3ß-hydroxy-27-oxo-olean-12en-28-oic acid, and 3ß,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.