Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosumL.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.

Early maturity is one of the most important agronomical traits in potato breeding. To identify the DNA segment that codes for early maturity, a tetraploid potato segregation population of “Zhongshu 19” × “Zhongshu 3” was genetically analyzed, using a combination of high throughput simplified genome sequencing (2b-RAD) and bulked segregant analysis (BSA). The DNA segment related to the early-maturity trait was identified at the 3.7~4.2 Mb locus on the short arm of chromosome 5. Eight molecular markers were developed, of which five were closely linked to the early-maturity trait loci. Additionally, 42 simple sequence repeats (SSR) markers were constructed based on the reference sequence of Solanum tuberosum group Phureja DM1-3 516 R44 (DM). Using the TetraploidMap software, the linkage map of chromosome 5 was constructed with 50 markers. The total map length was 172 centiMorgan (cM), with an average genetic distance of 3.44 cM. Correlating molecular and phenotypic data of the segregating population, the mapped Quantitative Trait Loci (QTL) on the short arm of chromosome 5 contributed to 33.55% of the early-maturity phenotype. The early-maturity QTL was located at 84 cM, flanked by the SSR5-85-1 and SCAR5-8 markers. The QTL was fine-mapped to 471 kb. Using DNA sequence annotation, 34 genes were identified in this region, 12 of them with unknown function. Among the other 22 annotated genes, E3 ubiquitin ligase gene PUB14 could be related to maturity and regulation of tuber formation. The constructed QTL map is a useful basic tool for the cloning of early-maturity related genes in tetraploid potatoes.

Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R²) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R² of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R² as high as 0.87, while the R² using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale.

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.

Maturity is a key trait for breeders to identify potato cultivars suitable to grow in different latitudes. However, the molecular mechanism regulating maturity remains unclear. In this study, we performed a grafting experiment using the early-maturing cultivar Zhongshu 5 (Z5) and the late-maturing cultivar Zhongshu 18 (Z18) and found that abscisic acid (ABA) and salicylic acid (SA) positively regulate the early maturity of potato, while indole-3-acetic acid (IAA) negatively regulated early maturity. A total of 43 long-distance transport mRNAs are observed to be involved in early maturity, and 292 long-distance transport mRNAs involved in late maturity were identified using RNA sequencing. Specifically, StMADS18, StSWEET10C, and StSWEET11 are detected to be candidate genes for their association with potato early maturity. Metabolomic data analysis shows a significant increase in phenolic acid and flavonoid contents increased in the scion of the early-maturing cultivar Z5, but a significant decrease in amino acid, phenolic acid, and alkaloid contents increased in the scion of the late-maturing cultivar Z18. This work reveals a significant association between the maturity of tetraploid cultivated potato and long-distance transport signal molecules and provides useful data for assessing the molecular mechanisms underlying the maturity of potato plants and for breeding early-maturing potato cultivars.

The temporal dynamics of canopy growth are closely related to the accumulation and distribution of plant dry matter. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have been increasingly adopted in crop growth monitoring. In this study, two potato varieties were used as materials, and treated with different combinations of nitrogen forms (nitrate and ammonium) and application rates (0, 150, and 300 kg ha⁻¹). A canopy development model was then constructed using low-cost time-series RGB imagery acquired by UAV. The objectives of this study were to quantify the variation in canopy development parameters under different nitrogen treatments and to explore the model parameters that represent the dynamics of plant dry matter accumulation, as well as those that contribute significantly to yield. The results showed that, except for the thermal time to canopy senescence (t₂), other parameters of the potato canopy development model exhibited varying degrees of variation under different nitrogen treatments. The model parameters were more sensitive to nitrogen forms, such as ammonium and nitrate, than to application rates. The integral area (A_t) under the canopy development curve had a direct effect on plant dry matter accumulation (path coefficient of 0.78), and the two were significantly positively correlated (Pearson correlation coefficient of 0.93). Integral area at peak flowering (A_tII) was significantly correlated with yield for both single and mixed potato varieties, having the greatest effect on yield (total effect of 1.717). In conclusion, UAV-acquired time-series RGB imagery could effectively quantify the variation of potato canopy development parameters under different nitrogen treatments and monitor the dynamic changes in plant dry matter accumulation. The regulation of canopy development parameters is of great importance and practical value for optimizing nitrogen management strategies and improving yield.