Aeroponic mist is an ideal system to study cassava storage roots
There is broad interest in developing a cassava root phenotyping platform to study RSA traits under controlled environmental conditions, which are predictive of field performance. However, controlled environment-based studies in cassava are few [27], and no specific storage root phenotyping method exists. ‘Shovelomics’ (uncovering roots or uprooting plants in the field) in cassava is laborious and requires destructive sampling; real-time monitoring is almost impossible. The heterogeneity in the soil physical and chemical composition that can affect the RSA of field-grown cassava at different locations is another factor confounding the effect, due to G × E [28]. The methods exploited to visualize RSA vary from one crop to another, based mainly on the crop duration, root morphology, and root function. Most advances in root phenotyping methods were achieved in cereals such as rice. The root system architecture of these crops is not very complex, and they are easy to grow hydroponically [9, 20]. However, root phenotyping of root crops like cassava is difficult due to complexities such as multiple functionality—e.g. storage, anchoring and seeking/uptake of water and nutrients.
In the present study, we attempted to design a low-cost and sensitive platform able to support studies of cassava root system architecture. Since storage roots did not bulk in the fully submerged conditions (hydroponics) in previous studies, we attempted different methods to obtain storage roots. Initially, we constructed semi-aeroponic systems to establish the SR development protocol. Based on our previous [19] and preliminary research, we found that four main factors (nutrients, air, light, and mist pressure on storage roots) contribute to storage root formation under controlled conditions.
Nutrients: Through our preliminary studies [19], we have found that nitrogen forms were influencing storage root initiation and bulking. Since cassava is an aerobic crop, we used a NO3− enriched nutrient solution to induce roots. When we continued to use the sole NO3− N enriched solution in the experiment, we did not obtain any storage root bulking; the only effect noted was that FR became elongated. Our nitrogen nutrient studies confirmed that NO3− enhanced early fibrous root vigor (first 30 days); however, the roots were sensitive to sole NH4+ enriched nutrient solution. When we switched to NH4NO3 (50–50%) enriched nutrient solution in the system, we started to observe storage root initiation and bulking. Different N sources (NH4+, NO3−) and their combination (NH4NO3) were tested to improve plant growth reported in potato [29, 30]. Our study also confirmed that storage root initiation was induced when we switched to an NH4NO3 (50–50%) enriched solution. Several authors have reported the influence of nitrogen sources on crop growth and root vigor [31, 32]. Ammonium ions (NH4+) supplied as the sole nitrogen source inhibited crop growth compared to NH4NO3 and sole NO3− [33, 34]. Crop growth seems to improve when a combination of NH4+ and NO3− is taken up by the plant.
Air: SR initiation and bulking need aeration; submerged (hydroponic) conditions did not induce SR bulking.
Light: The storage root bulking process needs darkness. Gentle mist pressure: Although the semi-aeroponic system was successful in developing storage roots, we found issues of late root initiation and bulking which do not reflect the field reality. To improve the system further, we tested the dripponics system. The drip system had a minor effect on the development of the storage roots compared to the aeroponic spray system (Fig. 2). The drip system also did not produce any pressure on the growing roots, mainly because the water flow had little contact with a large part of the surface area of the root and therefore reduced the uptake of nutrients and oxygen [35]. However, in the case of the aeroponic mist system, regular mist cycles keep the roots moist and avoid drying out, as well as providing the consistent nutrients with gentle mist pressure.
Our aeroponics system uses a mist of nutrients over the roots inside a dark growth chamber. The consistent mist spray interval and gentle mist pressure on roots helps to initiate the storage roots faster, as long as the root chamber is kept at ideal temperatures. We recorded and monitored the chamber temperature periodically, and we observed that the noon-time chamber temperature reached up to 31 °C, depending on the greenhouse temperature and climate. However, the storage root development of cassava was normal even at somewhat elevated temperature; we did not notice any root death. Usually in aeroponics systems, when moisture is deficient in the root zone, plants can begin to senesce and transpire less water. Therefore, we ensured that the misting schedule would prevent drying of the roots; the chamber was statically controlled for mist for 30 s every 20 min.
Some advantages of our aeroponic mist system are that it is custom-made, is low-cost, uses nutrient solution efficiently, and provides roots with maximum oxygen. It is closer to the soil environment than hydroponics and the crops grow more rapidly [36]. In addition, the aeroponic mist setup described in this paper enables easy and local root sampling, without destroying the rest of the plant. Time series experiments are thus feasible, e.g., for cytology or gene-expression analyses which lead to novel gene identification regulating storage root yield in cassava.
NAA seed treatment has the potential to accelerate root bulking
To prove the utility of the developed aeroponic mist system, we carried out a study to explore some of the key questions still unanswered: what makes cassava roots initiate the starch storage process, and what are the key factors that enhance this critical process?. Based on earlier results on other root crops [37, 38], we hypothesized that auxin has potential to accelerate starch storage in cassava in an aeroponic system. Our aeroponic mist system demonstrated the influence of NAA on root development (Fig. 3). Auxin may have several functions in the initiation, bulking and growth of storage roots [39]. In the present study, NAA-treated plants were significantly different in terms of storage root differentiation (Additional file 2: Fig. S4), bulking (Additional file 2: Fig. S5) and storage root yield than non-treated (Fig. 3c, f).
To test the predictability of the aeroponics system, we also confirmed results through a field experiment. Our field experiment results revealed that storage root yields obtained from the field grown plants were higher compared to the aeroponic-grown plants (Figs. 3f, 4d). However, the genotype responses to auxin effect on SR were the same as those obtained from aeroponics, which indicates the predictability of the aeroponic mist system. It is evident that the aeroponic system lacks interaction with the soil and other aspects such as mechanical, electrochemical and biological factors that affect the developing roots [40, 41], so it is logical to suggest that the lack of these interactions could explain the higher yields in the field. However, we observed that the time and mode of differentiation from fibrous roots to storage roots was almost the same in both aeroponics and field situations (Additional file 2: Fig. S6, S7).
Our histological results showed that the number of parenchymatic and secondary xylem parenchyma cells were significantly increased in auxin-treated (NAA+) plants (Fig. 5a–j). This increase resulted in earlier thickening and bulking than in non-treated plants (NAA−). The bulking of a fibrous root destined to become a storage root happens through secondary growth development, causing the proliferation of secondary xylem parenchyma in which starch is stored. Taken together, these results demonstrated that auxin plays a role in the formation of storage roots by activating the proliferation of parenchymatic and secondary xylem parenchyma cells to induce the initial thickening growth of storage roots. This finding agrees with results reported in earlier studies of sweet potato [42]. Noh et al. [42] reported that a MADS-box protein copy DNA, SRD1 boosts the proliferation of the metaxylem and cambium cells through the auxin-dependent initial bulking and growth of storage roots. However, no literature was available on the genetic control of adventitious and lateral roots in cassava [14], due to the lack of a suitable model system to study those mechanisms.
Towards high-throughput root image analysis platform
To provide a complete package to the cassava root research community, we also developed a simple image analysis protocol to estimate storage root traits in real-time. We used the ImageJ program because it is open access, reliable and easy to use [43]. Similarly, SmartRoot is open access, and is a very intuitive, semi-automatic tool with better management of output files compared to other programs [44]. The primary objective of this process is to estimate length, diameter and volume of storage roots without sacrificing plants, thus allowing us to study the root dynamics over time. The semi-automatic image analysis developed from this research takes approximately 50 s per photo using macros. We utilized the color to classify storage (dark brown) and fibrous (light) roots [45]. However, it is important to mention that some SR can have a color very similar to that of FR; in this case, the segmentation was done manually based on root thickness, which took more time. In the future, it’s viable to develop machine learning models to differentiate storage roots based on color change and thickness. We used natural light as a source of illumination when acquiring images, and it is essential to bear in mind that this light is subject to natural variations that can change the characteristics of the image, which affects its subsequent analysis [46]. We solved this issue by taking the images during a fixed hour of the day (in the morning), and we also always took a photograph of a standard reference to adjust to a standard illumination level. Cassava roots are complex structures comprising fibrous and storage roots. The number and size of the storage roots are potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots is 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. There is a need for more automated storage root image analysis approaches to support the current phenotyping hardware, making the analysis more high-throughput. We will investigate machine learning approaches to remove some of the manual steps from the current procedure. Our ultimate aim is to develop a fully-automated, image-based phenotyping system to automatically count storage roots in cassava, including early bulking storage roots (usually from 0 to 2.5 months). The developed root phenotyping systems and image analysis protocols from this study can also be easily transferable to other root and tuber crops.