One of the most significant environmental factors affecting plant growth, development and productivity is salt stress. The damage caused by salt to plants mainly includes ionic, osmotic and secondary stresses, while the plants adapt to salt stress through multiple biochemical and molecular pathways. Tomato (Solanum lycopersicum L.) is one of the most widely cultivated vegetable crops and a model dicot plant. It is moderately sensitive to salinity throughout the period of growth and development. Biotechnological efforts to improve tomato salt tolerance hinge on a synthesized understanding of the mechanisms underlying salinity tolerance. This review provides a comprehensive review of major advances on the mechanisms controlling salt tolerance of tomato in terms of sensing and signaling, adaptive responses, and epigenetic regulation. Additionally, we discussed the potential application of these mechanisms in improving salt tolerance of tomato, including genetic engineering, marker-assisted selection, and eco-sustainable approaches.

Optimum irrigation scheduling is important for ensuring high yield and water productivity in substrate-cultivated vegetables and is determined based on information such as substrate water content, meteorological parameters, and crop growth. The aim of this study was to determine a precise irrigation schedule for coconut coir culture in a solar greenhouse by comparing the irrigation, evapotranspiration (ET), substrate water content (VWC), as well as the crop growth indices and yield of cucumber, and irrigation water productivity (IWP) under three irrigation schedules: the soil moisture sensor-based method (T-VWC), the accumulated radiation combined with soil moisture sensor-based method (Rn-VWC), and the crop evapotranspiration estimated method using the hourly PM-ETo equation with an improved calculation of Kc (T-ETc). The results showed that the daily irrigation and evapotranspiration amount were the highest under T-VWC treatment, while the lowest under T-ETc treatment. In different meteorological environments, the change in irrigation amount was more consistent with the ET,and the VWC was relatively stable in T-ETc treatment compared with that under T-VWC or Rn-VWC treatments. The plant height, leaves number, leaf area, and stem diameter of T-VWC and Rn-VWC treatments were higher than those of the T-ETc treatments, but there was no significant difference in cucumber yield. Compared with the T-VWC treatment, total irrigation amount under Rn-VWC and T-ETc treatments significantly decreased by 25.75% and 34.04%, respectively ( $$\hbox {P}<0.05$$P < 0.05 ). The highest IWP values of 25.07 kg m $$^{-3}$$- 3 was achieved from T-ETc treatment with significantly increasing by 44.33% compared to the T-VWC treatment (17.37 kg m $$^{-3}$$- 3 ). In summary, the T-ETc treatment allowed more reasonable irrigation management and was appropriate for growing cucumber in coconut coir culture.

In order to compare and analyze the chloroplast (cp) genomes of tomato germplasms and understand their phylogenetic relationships, the cp genomes of 29 tomato germplasms were sequenced and analyzed in this study. The results showed highly conserved characteristics in structure, number of gene and intron, inverted repeat regions, and repeat sequences among the 29 cp genomes. Moreover, single-nucleotide polymorphism (SNP) loci with high polymorphism located at 17 fragments were selected as candidate SNP markers for future studies. In the phylogenetic tree, the cp genomes of tomatoes were clustered into two major clades, and the genetic relationship between S. pimpinellifolium and S. lycopersicum was very close. In addition, only rps15 showed the highest average K_A/K_S ratio in the analysis of adaptive evolution, which was strongly positively selected. It may be very important for the study of adaptive evolution and breeding of tomato. In general, this study provides valuable information for further study of phylogenetic relationships, evolution, germplasm identification, and molecular marker-assisted selection breeding of tomato.

In order to screen out the most stable reference genes in tomatoes under powdery mildew (PM) stress and study the expression of related genes in the interaction between tomato and PM more accurately, this study will provide a calibration basis for the expression of related functional genes. In this study, the expression stabilities of eight tomato candidate reference genes of EF1α, L33, Act, Ubi, GAPDH, UK, CAC and TIP41 in susceptible tomato and resistant tomatoes under PM stress were ranked using four different computation programs, including geNorm, Normfinder, BestKeeper and the comparative ∆CT method. Then RefFinder was used to analyze the ranking results of four kinds of software comprehensively. Finally, the selected reference genes were validated by the target gene SlMLO1. The results of geNorm showed that the normalization of qRT-PCR using two reference genes could meet the requirements. The comprehensive analysis of RefFinder showed that the most stable reference genes were Act and EF1α for both tomato varieties. The combination of Act and GAPDH was most stable in susceptible tomato ‘MM’. The combination of Act and EF1α was most stable in resistant tomato ‘62579′. Generally, the Act was the most stable reference gene in the two tomato varieties under PM stress. This study will lay a foundation for the related functional gene expression research in tomatoes under PM stress.

In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon “Boyang 91” was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m²·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (R_P = 0.824, RMSEP = 1.973). Ten characteristic wavelengths ( 396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories.