Vegetables are plants or portion of plants cultivated for food with a savory flavor and considerably nutritional value with little protein or fat. The yield and quality of vegetable crops are affected by various abiotic stresses, such as drought, salinity and low and high temperatures. Higher plants have evolved a series of complex responses in order to adapt to a single or multiple stresses. Recently, high-throughput sequencing has brought powerful and efficient research tools that can lead to a better understanding of the molecular mechanisms behind stress in plants. Many molecular markers, functional and regulatory genes have been discovered based on the genome sequencing. The new technologies, such as transcriptome analysis, digital gene expression, deep sequencing of small RNAs, proteomics, metabolomics, etc. should pave new avenues for studying stress resistance in vegetable crops. Here, we review recent progresses in the transcriptomic, proteomic, metabolomic and functional genomic approaches that have been used in the field of abiotic stress with vegetable crops. The perspectives on future research and improvement of vegetable crops through applied genomics are provided.

Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other elements can significantly affect automated yield estimation and picking in small target apple orchards in natural settings. This study uses the MinneApple public dataset, which is processed to construct a dataset of 829 images with complex weather, including 232 images of fog scenarios and 236 images of rain scenarios, and proposes a lightweight detection algorithm based on the upgraded YOLOv7-tiny. In this study, a backbone network was constructed by adding skip connections to shallow features, using P2BiFPN for multi-scale feature fusion and feature reuse at the neck, and incorporating a lightweight ULSAM attention mechanism to reduce the loss of small target features, focusing on the correct target and discard redundant features, thereby improving detection accuracy. The experimental results demonstrate that the model has an mAP of 80.4% and a loss rate of 0.0316. The mAP is 5.5% higher than the original model, and the model size is reduced by 15.81%, reducing the requirement for equipment; In terms of counts, the MAE and RMSE are 2.737 and 4.220, respectively, which are 5.69% and 8.97% lower than the original model. Because of its improved performance and stronger robustness, this experimental model offers fresh perspectives on hardware deployment and orchard yield estimation.

Oxidant stress resistance in Conyza bonariensis and wheat (Triticum aestivum) has been correlated with high levels of antioxidant enzyme activities. Additionally, external oxidant stresses can increase a plant's levels of the enzymes of polyamine biosynthesis and polyamines, especially putrescine. We investigated the constitutive relationships between putrescine, putrescine-generating enzymes, and oxidant stress resistance in wheat and C. bonariensis. Putrescine was Constitutively elevated (2.5- to 5.7-fold) in 2-week-old-resistant wheat and C. bonariensis biotypes, which correlated with a 10- to 15-fold increase in paraquat oxidant resistance. Arginine and ornithine decarboxylase activities doubled, along with higher putrescine levels in resistant C. bonariensis. The variations in levels of putrescine and arginine and ornithine decarboxylase activities paralleled the constitutive variation of antioxidant enzymes, as well as oxidant resistance. Higher levels of both putrescine and antioxidant enzyme activities occurred during a peak of oxidant resistance at 10 weeks, when paraquat resistance in C. bonariensis plants is >50-fold greater than in the sensitive biotype. Application of 100 [mu]M putrescine can double oxidant-stress resistance in the resistant C. bonariensis. Putrescine may play an important role in contributing to the base level of oxidant resistance found at the nonpeak period.

Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications.