Thermoinhibition refers to the inability of seeds to germinate when inhibited by high temperatures, but when environmental conditions return to normal, the seeds are able to germinate rapidly again, which is different from thermodormancy. Meanwhile, with global warming, the effect of the thermoinhibition phenomenon on the yield and quality of crops in agricultural production is becoming common. Lettuce, as a horticultural crop sensitive to high temperature, is particularly susceptible to the effects of thermoinhibition, resulting in yield reduction. Therefore, it is crucial to elucidate the intrinsic mechanism of action of thermoinhibition in lettuce seeds. This review mainly outlines several factors affecting thermoinhibition of lettuce seed germination, including endosperm hardening, alteration of endogenous or exogenous phytohormone concentrations, action of photosensitizing pigments, production and inhibition of metabolites, maternal effects, genetic expression, and other physical and chemical factors. Finally, we also discuss the challenges and potential of lettuce seed germination thermoinhibition research. The purpose of this study is to provide theoretical support for future research on lettuce seed germination thermoinhibition, and with the aim of revealing the mechanisms and effects behind lettuce seed thermoinhibition. This will enable the identification of more methods to alleviate seed thermoinhibition or the development of superior heat-tolerant lettuce seeds.

Low-temperature germinability (LTG) is an important agronomic trait that can affect the planting time, planting area, and grain yield of staple crops, such as rice. However, the genetic mechanism of LTG is still unclear. In this study, a multi-parental permanent population with 208 single segment substitution lines (SSSLs) was used to conduct a genetic dissection for LTG across four cropping seasons. LTG was a typical quantitative trait with a high combined broad-sense heritability of 0.71. By comparison with the recipient parent, Huajingxian74, 24 SSSLs were identified as carrying LTG QTLs, which were further merged into integrated QTLs with shorter genetic distances by substitution mapping. Finally, 14 LTG QTLs were mapped on ten chromosomes, including seven positive-effect and seven negative-effect QTLs, with additive effect contributions ranging from 19.2 to 39.9%. qLTG3a, a main-effect and novel QTL, was confirmed by bulk segregant analysis using an F₂ segregating population, and five key recombinants were selected to develop F₃ populations for progeny testing. Marker-trait association analysis fine mapped qLTG3a to a 332.7-kb physical region between markers M6026 and M6341. Within this interval, 40 annotated genes were revealed, and three genes (Os03g0213300, Os03g0214400, and Os03g0214600) were considered as pivotal candidate genes for qLTG3a based on their sequence variations and expression patterns. Besides low temperature, qLTG3a can also enhance seed germination under standard temperature and osmotic stress. In summary, this study identified some genetic factors regulating LTG and opened a new window for breeding elite direct-seeded rice varieties. It will help reduce the climate risk in the production process of rice, which is of great significance to ensuring food security.

Accurate identification of high-quality seeds is crucial for maintaining superior crop traits. Lettuce is widely consumed vegetable with diverse varieties, however, the traditionally identification methods are both time-consuming and labor-intensive. This study explores feasibility of rapid, non-destructive identification of different lettuce varieties using multispectral imaging combined with machine learning. We firstly collected seed morphological and spectral data from 15 lettuce varieties using multispectral imaging. Then we applied Support Vector Machine (SVM), Random Forest (RF), and Back-Propagation Neural Network (BP), Linear Discriminant Analysis (LDA) for variety identification. The results demonstrated that multispectral imaging combined with machine learning models, effectively distinguished different lettuce seed varieties. The LDA model based on morphological and spectral fusion feature data performed best, and the average classification accuracy was 92.7 %. In the batch validation, the LDA model achieved an accuracy of 93.2 %.This method reduces cost and improves efficiency, showing great potential for seed identification in other crops.