A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta)

Supplementation with Manihot esculenta Crantz (Cassava) leaves' extract prevents recognition memory deficits and hippocampal antioxidant dysfunction induced by Amyloid-β

OBJECTIVE

The Manihot esculenta Crantz (Cassava) is a typical South American plant rich in nutrients and energetic compounds. Lately, our group has shown that non-pharmacological interventions with natural antioxidants present different neuroprotective effects on oxidative balance and memory deficits in AD-like animal models. Here, our objective was to evaluate the neuroprotective effects of Cassava leaves' extract (CAS) in an AD-like model induced by amyloid-beta (Aβ) 25-35 peptide.

METHODS

Male Wistar rats (n = 40; 60 days old) were subjected to 10 days of CAS supplementation; then, we injected 2 μL Aβ 25-35 in the hippocampus by stereotaxic surgery. Ten days later, we evaluated object recognition (OR) memory. Cassavas' total polyphenols, flavonoids, and condensed tannins content were measured, as well as hippocampal lipid peroxidation and total antioxidant capacity.

RESULTS

CAS protected against Aβ-induced OR memory deficits. In addition, Aβ promoted antioxidant capacity decrease, while CAS was able to prevent it, in addition to diminishing lipoperoxidation compared to Aβ.

DISCUSSION

We show that treatment with Cassava leaves' extract before AD induction prevents recognition memory deficits related to Aβ hippocampal injection. At least part of these effects can be related to the Cassava leaves' extract supplementation effects on diminishing lipid peroxidation and preventing a decrease in the hippocampal total antioxidant capacity in the hippocampus of AD-like animals without adverse effects. Once cassavais a plant of warm and dry ground that can adapt to growon various soil types and seems to resist several insects, our results enable Cassava to be considered asa potential preventive intervention to avoid or minimizeAD-induced memory deficits worldwide.

Progressive drought alters the root exudate metabolome and differentially activates metabolic pathways in cotton (Gossypium hirsutum)

Root exudates comprise various primary and secondary metabolites that are responsive to plant stressors, including drought. As increasing drought episodes are predicted with climate change, identifying shifts in the metabolome profile of drought-induced root exudation is necessary to understand the molecular interactions that govern the relationships between plants, microbiomes, and the environment, which will ultimately aid in developing strategies for sustainable agriculture management. This study utilized an aeroponic system to simulate progressive drought and recovery while non-destructively collecting cotton (Gossypium hirsutum) root exudates. The molecular composition of the collected root exudates was characterized by untargeted metabolomics using Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) and mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Over 700 unique drought-induced metabolites were identified throughout the water-deficit phase. Potential KEGG pathways and KEGG modules associated with the biosynthesis of flavonoid compounds, plant hormones (abscisic acid and jasmonic acid), and other secondary metabolites were highly induced under severe drought, but not at the wilting point. Additionally, the associated precursors of these metabolites, such as amino acids (phenylalanine and tyrosine), phenylpropanoids, and carotenoids, were also mapped. The potential biochemical transformations were further calculated using the data generated by FT-ICR MS. Under severe drought stress, the highest number of potential biochemical transformations, including methylation, ethyl addition, and oxidation/hydroxylation, were identified, many of which are known reactions in some of the mapped pathways. With the application of FT-ICR MS, we revealed the dynamics of drought-induced secondary metabolites in root exudates in response to drought, providing valuable information for drought-tolerance strategies in cotton.

Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots

The root is an important organ for crops to absorb water and nutrients. Complete and accurate acquisition of root phenotype information is important in root phenomics research. The in situ root research method can obtain root images without destroying the roots. In the image, some of the roots are vulnerable to soil shading, which severely fractures the root system and diminishes its structural integrity. The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored. Therefore, based on the in situ root image of cotton, this study proposes a root segmentation and reconstruction strategy, improves the UNet model, and achieves precise segmentation. It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two. The research results show that the improved UNet model has an accuracy of 99.2%, mIOU of 87.03%, and F1 of 92.63%. The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%. This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network. It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems, also realizes the restoration of the integrity of the in situ root image, and provides a new method for in situ root phenotype study.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Recent advances in methods for in situ root phenotyping

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

Fifty years of a public cassava breeding program: evolution of breeding objectives, methods, and decision-making processes

This paper reviews and analyzes key features from cassava breeding at the International Center for Tropical Agriculture (CIAT) over 50 years and draws lessons for public breeding efforts broadly. The breeding team, jointly with national program partners and the private processing sector, defined breeding objectives and guiding business plans. These have evolved through the decades and currently focus on four global product profiles. The recurrent selection method also evolved and included innovations such as estimation of phenotypic breeding values, increasing the number of locations in the first stage of agronomic evaluations, gradual reduction of the duration of breeding cycles (including rapid cycling for high-heritability traits), the development of protocols for the induction of flowering, and the introduction of genome-wide predictions. The impact of cassava breeding depends significantly on the type of target markets. When roots are used for large processing facilities for starch, animal feeding or ethanol production (such as in SE Asia), the adoption of improved varieties is nearly universal and productivity at the regional scale increases significantly. When markets and relevant infrastructure are weak or considerable proportion of the production goes for local artisanal processing and on-farm consumption, the impact has been lower. The potential of novel breeding tools needs to be properly assessed for the most effective allocation of resources. Finally, a brief summary of challenges and opportunities for the future of cassava breeding is presented. The paper describes multiple ways that public and private sector breeding programs can learn from each other to optimize success.

Auxin: An emerging regulator of tuber and storage root development

Many tuber and storage root crops owing to their high nutritional values offer high potential to overcome food security issues. The lack of information regarding molecular mechanisms that govern belowground storage organ development (except a tuber crop, potato) has limited the application of biotechnological strategies for improving storage crop yield. Phytohormones like gibberellin and cytokinin are known to play a crucial role in governing potato tuber development. Another phytohormone, auxin has been shown to induce tuber initiation and growth, and its crosstalk with gibberellin and strigolactone in a belowground modified stem (stolon) contributes to the overall potato tuber yield. In this review, we describe the crucial role of auxin biology in development of potato tubers. Considering the emerging reports from commercially important storage root crops (sweet potato, cassava, carrot, sugar beet and radish), we propose the function of auxin and related gene regulatory network in storage root development. The pattern of auxin content of stolon during various stages of potato tuber formation appears to be consistent with its level in various developmental stages of storage roots. We have also put-forward the potential of three-way interaction between auxin, strigolactone and mycorrhizal fungi in tuber and storage root development. Overall, we propose that auxin gene regulatory network and its crosstalk with other phytohormones in stolons/roots could govern belowground tuber and storage root development.

Getting to the roots of aeroponic indoor farming

Vertical farming is a type of indoor agriculture where plants are cultivated in stacked systems. It forms a rapidly growing sector with new emerging technologies. Indoor farms often use soil-free techniques such as hydroponics and aeroponics. Aeroponics involves the application to roots of a nutrient aerosol, which can lead to greater plant productivity than hydroponic cultivation. Aeroponics is thought to resolve a variety of plant physiological constraints that occur within hydroponic systems. We synthesize existing studies of the physiology and development of crops cultivated under aeroponic conditions and identify key knowledge gaps. We identify future research areas to accelerate the sustainable intensification of vertical farming using aeroponic systems.

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz)

BACKGROUND

Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing.

RESULTS

To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively.

CONCLUSION

UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

Comprehensive Genome-Wide Association Analysis Reveals the Genetic Basis of Root System Architecture in Soybean

Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.