Potato Tuber Formation Research Articles

Research on Estimating Potato Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop’s entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.

Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data

The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field.

Yield reduction caused by elevated temperatures and high nitrogen fertilization is mitigated by SP6A overexpression in potato (Solanum tuberosum L.).

Potatoes (Solanum tuberosum L.) are a fundamental staple for millions of people worldwide. They provide essential amino acids, vitamins, and starch - a vital component of the human diet, providing energy and serving as a source of fiber. Unfortunately, global warming is posing a severe threat to this crop, leading to significant yield losses, and thereby endangering global food security. Industrial agriculture traditionally relies on excessive nitrogen (N) fertilization to boost yields. However, it remains uncertain whether this is effective in combating heat-related yield losses of potato. Therefore, our study aimed to investigate the combinatory effects of heat stress and N fertilization on potato tuber formation. We demonstrate that N levels and heat significantly impact tuber development. The combination of high N and heat delays tuberization, while N deficiency initiates early tuberization, likely through starvation-induced signals, independent of SELF-PRUNING 6A (SP6A), a critical regulator of tuberization. We also found that high N levels in combination with heat reduce tuber yield rather than improve it. However, our study revealed that SP6A overexpression can promote tuberization under these inhibiting conditions. By utilizing the excess of N for accumulating tuber biomass, SP6A overexpressing plants exhibit a shift in biomass distribution towards the tubers. This results in an increased yield compared to wild-type plants. Our results highlight the role of SP6A overexpression as a viable strategy for ensuring stable potato yields in the face of global warming. As such, our findings provide insights into the complex factors impacting potato crop productivity.

Research on potato (Solanum tuberosum L.) nitrogen nutrition diagnosis based on hyperspectral data

AbstractReducing the overapplication of nitrogen fertilizers to potatoes (Solanum tuberosum L.) can reduce production costs and their impact on the environment. One approach to produce these impacts is to reduce overapplications of fertilizers by using the nitrogen nutrition index (NNI = plant nitrogen concentration/critical nitrogen concentration) as a basis for in‐season nitrogen recommendations. The objective of this study was to create a remote sensing–based algorithm to estimate NNI. This study collected hyperspectral data (350–1830 nm) during the potato tuber formation period in 2022 and 2023. The climate regime for the study area was a mid‐temperate semiarid continental monsoon; in our study, three different spectral parameter calculation methods were employed. First, the empirical vegetation index, determined through a fixed two‐band calculation. Second, the optimal vegetation index, computed on a band‐by‐band basis. Lastly, the trilateral spectral approach, wherein the indicators are typically associated with the red edge, blue edge, and green edge. The optimum vegetation index had the highest correlation with NNI. The support vector machine, random forest (RF), and back propagation neural network models were used to create NNI prediction models. All machine learning models effectively estimated NNI, and during validation, the R2 (coefficient of determination) was >0.700. In general, the RF model outperformed the other models and during validation had an R2 of 0.869, a root mean square error of 0.052, and a relative error of 5.504%. This study demonstrates the scalability, simplicity, and cost‐effectiveness of combining hyperspectral technology and machine learning for rapid potato NNI estimation.

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

StBIN2 Positively Regulates Potato Formation through Hormone and Sugar Signaling.

Potato is an important food crop worldwide. Brassinosteroids (BRs) are widely involved in plant growth and development, and BIN2 (brassinosteroid insensitive 2) is the negative regulator of their signal transduction. However, the function of BIN2 in the formation of potato tubers remains unclear. In this study, transgenic methods were used to regulate the expression level of StBIN2 in plants, and tuber related phenotypes were analyzed. The overexpression of StBIN2 significantly increased the number of potatoes formed per plant and the weight of potatoes in transgenic plants. In order to further explore the effect of StBIN2 on the formation of potato tubers, this study analyzed BRs, ABA hormone signal transduction, sucrose starch synthase activity, the expression levels of related genes, and interacting proteins. The results show that the overexpression of StBIN2 enhanced the downstream transmission of ABA signals. At the same time, the enzyme activity of the sugar transporter and the expression of synthetic genes were increased in potato plants overexpressing StBIN2, which also demonstrated the upregulation of sucrose and the expression of the starch synthesis gene. Apparently, StBIN2 affected the conversion and utilization of key substances such as glucose, sucrose, and starch in the process of potato formation so as to provide a material basis and energy preparation for forming potatoes. In addition, StBIN2 also promoted the expression of the tuber formation factors StSP6A and StS6K. Altogether, this investigation enriches the study on the mechanism through which StBIN2 regulates potato tuber formation and provides a theoretical basis for achieving a high and stable yield of potato.

Construction of heat stress regulation networks based on Illumina and SMRT sequencing data in potato.

Potato (Solanum tuberosum L.) is one of the most important tuber food crops in the world; however, the cultivated potatoes are susceptible to high temperature, by which potato production is adversely affected. Understanding the coping mechanism of potato to heat stress is essential to secure yield and expand adaptability under environmental conditions with rising temperature. However, the lack of heat-related information has significantly limited the identification and application of core genes. To gain deeper insights into heat tolerance genes, next-generation sequencing and single-molecule real-time sequencing were used to learn the transcriptional response of potato to heat stress and 13,159 differentially expressed genes (DEGs) were identified in this study. All DEGs were grouped into 12 clusters using the K-means clustering algorithm. Gene Ontology enrichment analysis revealed that they were involved in temperature signaling, phytohormone, and protein modification. Among them, there were 950 differentially expressed transcription factors (DETFs). According to the network analysis of DETFs at the sixth hour under heat stress, we found some genes that were previously reported to be associated with photoperiodic tuberization, StCO (CONSTANS), tuber formation, StBEL11 (BEL1-LIKE 11), and earliness in potato, StCDF1 (CYCLING DOF FACTOR 1) responding to temperature. Furthermore, we verified the relative expression levels using quantitative real-time polymerase chain reaction, and the results were consistent with the inferences from transcriptomes. In addition, there were 22,125 alternative splicing events and 2,048 long non-coding RNAs. The database and network established in this study will extend our understanding of potato response to heat stress. It ultimately provided valuable resources for molecular analysis of heat stress response in potato and cultivation of potato varieties with heat tolerance.

Estimating potassium in potato plants based on multispectral images acquired from unmanned aerial vehicles.

Plant potassium content (PKC) is a crucial indicator of crop potassium nutrient status and is vital in making informed fertilization decisions in the field. This study aims to enhance the accuracy of PKC estimation during key potato growth stages by using vegetation indices (VIs) and spatial structure features derived from UAV-based multispectral sensors. Specifically, the fraction of vegetation coverage (FVC), gray-level co-occurrence matrix texture, and multispectral VIs were extracted from multispectral images acquired at the potato tuber formation, tuber growth, and starch accumulation stages. Linear regression and stepwise multiple linear regression analyses were conducted to investigate how VIs, both individually and in combination with spatial structure features, affect potato PKC estimation. The findings lead to the following conclusions: (1) Estimating potato PKC using multispectral VIs is feasible but necessitates further enhancements in accuracy. (2) Augmenting VIs with either the FVC or texture features makes potato PKC estimation more accurate than when using single VIs. (3) Finally, integrating VIs with both the FVC and texture features improves the accuracy of potato PKC estimation, resulting in notable R 2 values of 0.63, 0.84, and 0.80 for the three fertility periods, respectively, with corresponding root mean square errors of 0.44%, 0.29%, and 0.25%. Overall, these results highlight the potential of integrating canopy spectral information and spatial-structure information obtained from multispectral sensors mounted on unmanned aerial vehicles for monitoring crop growth and assessing potassium nutrient status. These findings thus have significant implications for agricultural management.

Canonical and Alternative Auxin Signaling Systems in Mono-, Di-, and Tetraploid Potatoes.

It has long been known that the phytohormone auxin plays a promoting role in tuber formation and stress tolerance in potatoes. Our study aimed to identify and characterize the complete sets of auxin-related genes that presumably constitute the entire auxin signaling system in potato (Solanum tuberosum L.). The corresponding genes were retrieved from sequenced genomes of the doubled monoploid S. tuberosum DM1-3-516-R44 (DM) of the Phureja group, the heterozygous diploid line RH89-039-16 (RH), and the autotetraploid cultivar Otava. Both canonical and noncanonical auxin signaling pathways were considered. Phylogenetic and domain analyses of deduced proteins were supplemented by expression profiling and 3D molecular modeling. The canonical and ABP1-mediated pathways of auxin signaling appeared to be well conserved. The total number of potato genes/proteins presumably involved in canonical auxin signaling is 46 and 108 in monoploid DM and tetraploid Otava, respectively. Among the studied potatoes, spectra of expressed genes obviously associated with auxin signaling were partly cultivar-specific and quite different from analogous spectrum in Arabidopsis. Most of the noncanonical pathways found in Arabidopsis appeared to have low probability in potato. This was equally true for all cultivars used irrespective of their ploidy. Thus, some important features of the (noncanonical) auxin signaling pathways may be variable and species-specific.

H2O2 Participates in the Induction and Formation of Potato Tubers by Activating Tuberization-Related Signal Transduction Pathways

Reactive oxygen species (ROS), especially hydrogen peroxide, H2O2, act as signaling molecules to widely mediate growth, development, and stress response of plants. In the present study, internal ROS accumulation, effects of exogenous H2O2 treatment, the expression of the key tuberization-related genes, and the effect of knockout of Solanum tuberosum self-pruning 6A (StSP6A) on H2O2-induced tuber formation were investigated to elucidate whether and how H2O2 is involved in induction and formation of potato tubers using two diploid landraces, Solanum phureja and S. ajanhuiri. The results showed that there was a significant accumulation of ROS (including H2O2, superoxide anion, O2−, and total ROS) during tuber induction and formation in stolons/tubers, especially in the hook-like subapical part of stolons prior to tuberization, as detected by staining observation and quantitative measurement. Furthermore, exogenous H2O2 treatment significantly enhanced percentage of tuber formation. By contrast, addition of either the ROS inhibitor diphenyleneiodonium chloride (DPI) or H2O2 scavenger catalase (CAT) resulted in a decline of tuber formation. In addition, expression analysis of nine key tuberization-related genes demonstrated that the H2O2-induced tuberization could be associated with H2O2-controlled regulation of these tuberization- and signaling-pathway-related genes, especially StSP6A, which was dramatically up-regulated during the early stage of tuber induction and H2O2 treatment. When StSP6A was knocked out by CRISPR-Cas9-mediated genome editing, the tuberization frequency of StSP6A null-mutants became significantly lower at various H2O2 concentration treatments. These findings indicate that H2O2 accumulation in stolons might play an important role by acting as a signaling molecule to initiate tuber induction, H2O2-induced tuber formation is triggered by regulating the tuberization-related gene expression and activating signal transduction pathways, and StSP6A is a pivotal player in H2O2-induced tuber formation in potato.

Analysis of Potato Physiological and Molecular Adaptation in Response to Different Water and Nitrogen Combined Regimes.

Water and nitrogen are essential for potato growth and development. We aim to understand how potato adapts to changes in soil water and nitrogen content. Potato plant adaptations to changes in soil moisture and nitrogen levels were analyzed at the physiological and transcriptomic levels in four treatment groups: adequate nitrogen under drought, adequate nitrogen under sufficient irrigation, limited nitrogen under drought, and limited nitrogen under sufficient irrigation. Many light-capture pigment complex genes and oxygen release complex genes were differentially expressed in leaves when nitrogen levels were increased under drought conditions, and several genes encoding rate-limiting enzymes in the Calvin-Benson-Bassham cycle were up-regulated; furthermore, leaf stomatal conductance decreased, whereas the saturated vapor pressure difference and relative chlorophyll content in the chloroplasts increased. StSP6A, a key gene in potato tuber formation, was down-regulated in response to increased nitrogen application, and the stolon growth time was prolonged. Genes related to root nitrogen metabolism were highly expressed, and protein content in the tuber increased. Weighted gene co-expression network analysis (WGCNA) revealed 32 gene expression modules that responded to changes in water and nitrogen levels. A total of 34 key candidate genes were identified, and a preliminary molecular model of potato responses to alterations in soil water and nitrogen content was constructed.

THE INFLUENCE OF INTRODUCING ORGANIC NUTRIENTS AND GROWTH STIMULANT INTO THE SOIL IN DIFFERENT PERIODS ON THE GROWTH, DEVELOPMENT AND FORMATION OF POTATO TUBERS IN ARID CONDITIONS OF THE FOOTHILL ZONE

In post-forest, pebbly, carbonated, medium strength, brown soils of the foothill zone, where the annual rainfall is 531 mm (of which 111 mm falls in May and 13 mm in December), the humus content in the soil ranges from 3,5 to 4,5%, the average annual temperature is 11,8⁰ C, under dry conditions, the effect of introducing equivalent amounts of organo-mineral fertilizers and growth-promoting bio-liquid into the soil at different times on the growth, development, transition periods of phenological stages, intensity of potato tuber formation and yield of early potatoes was investigated. Experimental research has revealed the high efficiency of using a relatively small fractional amount of organomix (from sow and in the form of nutrition), and the full amount of bio-liquid in the form of soaking the planting material (3 days before planting), the same or more amount of one-time use, as well as equivalent amounts of mineral fertilizers accepted in the zone compared to the application.

Inducing potato tuber formation at low elevation of tropical region by foliar spray of PGR mixtures at different application times

Growing potato crops at low elevation in the tropical region may face many problems related to high temperature, such as an increase in endogen GA3 levels, causing the inhibition of tuber formation. Suppressing GA3 biosynthesis, by applying a mixture of plant growth regulators (PGRs), is of important. The objectives of this experiment were to study the best PGR mixtures, PGR application time, and their interaction in promoting tuber formation at low altitude of tropical areas in Indonesia. The experiment used a completely randomized design arranged in factorial (2 factors, 3 replications). The first factor was a mix of plant growth regulator solution (M), consisting of 4 levels, which were: M0 = No PGR; M1 = 10 ppm 2,4-D + 100 ppm Daminozide, M2 = M1 +10 ppm BAP, M3 = M1 + 25 ppm COU, and M4 = M1+ 500 ppm PBZ. The second factor was application time of the solutions (T), consisting of 3 levels, which were: T1 = 3, 4, 5, 6 weeks after emergence (WAE), T2 = 3, 4, 5 WAE, and T3 = 3, 4 WAE. The results showed that all of the PGR mixtures promoted tuber formation with 10 ppm 2,4-D + 100 ppm Daminozide + 500 ppm Paclobutrazol was found as the best PGR mixture. Moreover, the best time to spray the mixture was 3,4,5,6 WAE. Finally, the best interaction between PGR and application time was10 ppm 2,4-D + 100 ppm Daminozide + 10 ppm BAP applied at 3,4,5 WAE.

Multifunctional chemical inhibitors of the florigen activationcomplex discovered by structure-based high-throughput screening.

Structure-based high-throughput screening of chemical compounds that target protein-protein interactions (PPIs) is a promising technology for gaining insight into how plant development is regulated, leading to many potential agricultural applications. At present, there are no examples of using high-throughput screening to identify chemicals that target plant transcriptional complexes, some of which are responsible for regulating multiple physiological functions. Florigen, a protein encoded by FLOWERING LOCUS T (FT), was initially identified as a molecule that promotes flowering and has since been shown to regulate flowering and other developmental phenomena such as tuber formation in potato (Solanum tuberosum). FT functions as a component of the florigen activation complex (FAC) with a 14-3-3 scaffold protein and FD, a bZIP transcription factor that activates downstream gene expression. Although 14-3-3 is an important component of FAC, little is known about the function of the 14-3-3 protein itself. Here, we report the results of a high-throughput invitro fluorescence resonance energy transfer (FRET) screening of chemical libraries that enabled us to identify small molecules capable of inhibiting FAC formation. These molecules abrogate the invitro interaction between the 14-3-3 protein and the OsFD1 peptide, a rice (Oryza sativa) FD, by directly binding to the 14-3-3 protein. Treatment with S4, a specific hit molecule, strongly inhibited FAC activity and flowering in duckweed, tuber formation in potato, and branching in rice in a dose-dependent manner. Our results demonstrate that the high-throughput screening approach based on the three-dimensional structure of PPIs is suitable in plants. In this study, we have proposed good candidate compounds for future modification to obtain inhibitors of florigen-dependent processes through inhibition of FAC formation.

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices.

Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (H), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato H and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: R 2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R 2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R 2 = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.

Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes.

Crystal structure of potato 14-3-3 protein St14f revealed the importance of helix I in StFDL1 recognition

In potato (Solanum tuberosum L.), 14-3-3 protein forms a protein complex with the FLOWERING LOCUS T (FT)-like protein StSP6A and the FD-like protein StFDL1 to activate potato tuber formation. Eleven 14-3-3 isoforms were reported in potato, designated as St14a-k. In this study, the crystal structure of the free form of St14f was determined at 2.5 Å resolution. Three chains were included in the asymmetric unit of the St14f free form crystal, and the structural deviation among the three chain structures was found on the C-terminal helix H and I. The St14f free form structure in solution was also investigated by nuclear magnetic resonance (NMR) residual dipolar coupling analysis, and the chain B in the crystal structure was consistent with NMR data. Compared to other crystal structures, St14f helix I exhibited a different conformation with larger B-factor values. Larger B-factor values on helix I were also found in the 14-3-3 free form structure with higher solvent contents. The mutation in St14f Helix I stabilized the complex with StFDL1. These data clearly showed that the flexibility of helix I of 14-3-3 protein plays an important role in the recognition of target protein.

Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images

Above-ground biomass (AGB) is a significant phenotypic index for evaluating photosynthesis capacity, healthy growth, and estimating crop yield. Accurately monitoring the AGB helps improve agricultural fertilization management and optimize planting patterns. Numerous studies have confirmed that canopy spectrum saturation causes optical vegetation indices (VIs) to underestimate the AGB of crops at multiple growth periods. To solve this problem, the present research used a remote sensing method to obtain RGB images of potato tuber formation-, tuber growth-, and starch accumulation- periods by a high-definition digital camera sensor on the unmanned aerial vehicle (UAV). From the ultrahigh spatial resolution RGB images, we then extracted RGB-VIs, textures (based on the gray level co-occurrence matrix, GLCM), and crop height (Hdsm) and analyzed the correlation between the three image features and the potato AGB for single and multiple growth periods. Finally, we estimated potato AGB at multiple growth periods based on (1) RGB-VIs, (2) RGB-VIs + GLCM-based textures, (3) RGB-VIs + Hdsm, and (4) RGB-VIs + GLCM-based textures + Hdsm by applying multiple stepwise regression (MSR) and extreme learning machine (ELM). The results showed that (i) unlike the texture features of wheat and maize that increased with growth period, the texture features and crop height of the potato canopy both increased first and then decreased with the growth period. (ii) The potato AGB was poorly estimated when using RGB-VIs, CLCM-based textures, or Hdsm individually; (iii) combining GLCM-based textures, Hdsm, and RGB-VIs solved the problem of underestimating the high AGB values of potato samples by the RGB-VIs model alone. Therefore, combining GLCM-based textures, Hdsm, and RGB-VIs obtained from UAV digital images could enhance the accuracy of potato AGB estimation under high coverage.

Detection, in silico analysis and molecular diversity of phytoplasmas from solanaceous crops in Turkey

Phytoplasma-like symptoms of leaf yellowing and calyx malformation were observed in eggplant (Solanum melongena L.), upward leaves and fruit malformation in pepper (Capsicum annuum L.), and aerial tuber formation in potato (S. tuberosum L.) during the survey performed in the late season (August to September) of 2015 and 2016 in Van province (Turkey). A total of 100 samples were tested by nested-PCR using universal primer pairs to assess the sanitary status of the solanaceous crops and to characterise the phytoplasma isolates. Among them, seven samples resulted in a 1.25 kb DNA fragment, and five (two eggplants, two peppers, and one potato) were molecularly characterised (Accession No.: KY579357, KT595210, MF564267, MF564266, and MH683601). BLAST and the virtual restriction fragment length polymorphism (RFLP) analysis of 16S rRNA genes revealed the presence of two distinct phytoplasma infections in solanaceous crops: ‘Candidatus Phytoplasma trifolii’ a member of the clover proliferation group (16SrVI) and subgroup A and ‘Candidatus P. solani’ a member of the stolbur group (16SrXII) and subgroup A. The virtual RFLP analysis and calculated coefficients of RFLP pattern similarities further revealed a remarkable genetic diversity among the ‘Candidatus P. solani’ isolates infecting pepper (similarity coefficient of 0.90) and eggplant (similarity coefficients of 0.98 and 1.00) at the same geographical area. This is the first report of the natural occurrence of ‘Candidadtus P. trifolii’ in potato from the Eastern Anatolia region, Turkey.

​Factors Affect to Stolon Formation and Tuberization in Potato: A Review

Potato tuber formation is a complex process that is induced by a mobile signal called tuberigen. It involves several genes such as AtBMI, St 14-3-3s, StBEL, StBELL11, StBELL29, StBMI1-1, StCDF1, StCEN, StCO, StFDL1, StFT, StGA2ox1, StGA3ox2, StMSI1, StSP6A. This article focuses on important factors such as genetic factors, low temperature, high irradiation, low nitrogen, abscisic acid, chlormequat chloride, auxin, Jasmonic acid, cytokinin and paclobutrazol that induce tuber formation while ethylene, drought, low irradiation, high-temperature that reduce or inhibit tuber initiation.